Suni’s Weekly FDA Analysis Check-in Progress – Pupil Light Reflex
SG
2022-12-01
1 To Do:
- Try different number of basis functions start with 15 go to 30
- Write down mathematical interpretation of the plots.
- Impute with average for frames with missing values between known values
- Scalar on Function Regression
- Truncate all data to end of file with very little missing data
- Zsm - not needed
- Zlmat – use raw data not smooth because have already been smoothed
- Regression using VAS score to define “high” vs “not” in occasional
smokers
- Plot trajectories for “high” and “not” occcasional smokers
- Time from Consumption to test – WPD add FoSR
- Interaction term with user category and Post consumption Time to Test
- Create a document with results to send to the collaborators
2 Questions for Collaborators
- How much time does it take for the effects of marijuana to reduce?
3 Collaborator Meeting Notes
- Assess optimal threshold from logistic regression analyses (Youdon’s J)
- Significance test for AUC values? Are they really different from each other
- What’s the typical length of pulse for the test? (Varied randomly across participants)
- What’s the average test time? Can we convert frame rate to time (300 frames/sec)
- ROC analysis – need reasonably good specificity, maybe tease out some features that lead to good specificity
- Consider effects of “baseline” pupil size (initial small pupil size will lead to less change over time)
- Key results:
- Ability to differentiate smokers vs non-smokers in POST data
- No difference between occasional and daily user (e.g. no tolerance)
- Additional Analysis:
- Most individuals smoking in the study did not use a large dose and
had smaller cannabis effect than normal
- Examine if there are difference in user that reported feeling “stoned” vs not (VAS score 0-40)
- Among occasional user create a ordered categorical by THC amount
- Occasional user: 5 ng/ml THC
- Daily user: 25 ng/ml THC
4 Task Worked On From Last Week:
- Compare between AUCs for prediction models
- Convert frames to time (rate should be specified in Ben’s paper)
- Scalar on Function Regression
- Covariance between left and right eye
- (frame by frame)
- investigate time series correlation metrics
- Regression using VAS score to define “high” vs “not” in occasional smokers
4.2 Notes
- Send HTML of Rmarkdown to JW & AL by Tuesday night
- Ask quick questions through email (e.g. components of objects or error messages)
- Add PURPOSE and INTERPRETATIONS for each plot
- chart.Correlation fxn
- ’***: p < 0.001
- ’**: p < 0.01
- ’*: p < 0.05
- ’.: p < 0.10
- Do no use duration variable in scalar feature regressions
- \(R^2\) for pffr vs gam = 95% vs 25% – shows that gam is misspecified without RE for subject
- edf in pffr output of 1 = shift in intercept, 2 = non-linear effect
5 Background
Pupil size light reflex to a light test is a potential test to determine a person is under the influence of THC and be able to be use as a field sobriety measures. The goals of the analysis to:
5.1 Potential Topics
- Determine the variability of pupil light reflex in a sober population
- Determine the effectiveness of pupil light reflex to measure sobriety after smoking THC Question - dose response?
- Jointly model pupil light reflex and blink rate during a light test (another data set)
Pupil size light reflex to light does not habituated to THC use (smoking).
Time for smoking to post test maybe an important variable to model b/c:
- effects of THC reduce with time
- currently time from smoke period to post are all similar so we might not see an effect
There is a circadian pattern to pupil size (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7445830/).
5.2 Experimental Design
- Setting: Office
- Were the lights ON/OFF during testing (JW)
5.3 Features of Importance
- Point of maximal constriction – more dilation after smoking
- Rebound dilation
- Ideally the non-user data should have little change from pre to post but there may be some “habituation” to the test?
Currently this field does not use FDA, so any FDA in topic area may be publishable
6 Data summary
Data on pupil size during light reflex test. Pupil size was extracted at the image level based on image analysis techniques. Each test was performed simultaneously on right and left eye before and after THC use (smoking).
- USE percent change for analysis
- Start at frame 0
- End of test is not strictly defined (with FoS we shouldn’t need to define the end of the test)
7 FDA analysis pointers:
- Usually only interpret PC1 & PC2, check the percent of variance explained and decide from that if interpreting later PCs is important
- For prediction models: higher order PCs might be more useful
7.1 Questions:
- Can we translate frame into time? Do we need to? – No need to translate to time dimension, also time drift shouldn’t be an issue because duration of test is short
8 Data Preparation
ps <- read.csv(file.path(data_folder, ps_folder, "pupil_data_with_demo_20220926.csv"))
ps$demo_gender <- factor(ps$demo_gender,
levels = c(1,2),
labels = c("Male", "Female"))
ps$user_cat <- NA
ps$user_cat[ps$user_type == "non-user"] <- 0
ps$user_cat[ps$user_type == "occasional"] <- 1
ps$user_cat[ps$user_type == "daily"] <- 2
ps$user_cat <- factor(ps$user_cat,
levels = c(0,1,2),
labels = c("non-user",
"occasional",
"daily"))
ps$tp <- NA
ps$tp[ps$time == "pre2"] <- 0
ps$tp[ps$time == "post"] <- 1
ps$tp <- factor(ps$tp,
levels = c(0,1),
labels = c("pre2", "post"))
# str(ps)
## -- Not needed with Ben's revised file missing frame numbers recorded
# #impute frame number
# for(i in 2:(nrow(ps)-1)){
# if(is.na(ps$frame[i]) & is.na(ps$frame[i+1] - ps$frame[i-1])){
# ps$frame[i] <- ps$frame[i-1]+1
# }else(if(is.na(ps$frame[i]) & (ps$frame[i+1] - ps$frame[i-1]) == 2){
# ps$frame[i] <- ps$frame[i-1]+1
# }else(if(is.na(ps$frame[i]) & (ps$frame[i+1] - ps$frame[i-1]) > 2){
# if(abs(ps$percent_change[i] - ps$percent_change[i-1]) <
# abs(ps$percent_change[i+1] - ps$percent_change[i])){
# ps$frame[i] <- ps$frame[i-1]+1
# }else(ps$frame[i] <- ps$frame[i+1]-1)
# }
# )
# )
# }# total number of subjects
length(unique(ps$subject_id))## [1] 101# subject level data
pt.df <- unique(ps[, c("subject_id", "tp", "user_cat", "demo_age",
"demo_weight", "demo_height", "demo_gender", "thc")])
# # Demographics Table by User Category
# table1(~ demo_age + demo_weight + demo_height + demo_gender|user_cat,
# data = pt.df[pt.df$tp == "pre2", ])
# number of subjects by timepoint (pre/post)
table(pt.df$tp)##
## pre2 post
## 99 95There are more subjects in total than the by time point. Indicating incomplete data with some subjects missing “pre” and others missing “post” measurement.
Add scalar features for each participant-time point
scalar.feat <- read.csv(file.path(data_folder, ps_folder, "scalars_trim.csv"),
header = T, stringsAsFactors = F)
scalar.feat$subject_id <- substr(scalar.feat$timeid, 1, 7)
scalar.feat$tp <- substr(scalar.feat$timeid, 8, 11)
scalar.featR <- scalar.feat[scalar.feat$eye == "Right", ]
pt.df <- merge(pt.df, scalar.featR[, c("subject_id", "tp", "min_constriction",
"duration", "avg_end_percent", "slope",
"AUC", "eye")],
by = c("subject_id", "tp"))Reshape participant demog data to preserve pre and post THC levels and scalar features
pt.df.w <- reshape(pt.df,
timevar = "tp",
idvar = c("subject_id", "user_cat", "demo_age",
"demo_weight", "demo_height", "demo_gender", "eye"),
direction = "wide")
pt.df.w$thc.post[pt.df.w$user_cat == "non-user" & is.na(pt.df.w$thc.post)] <- 0
pt.df.w$thc_chg <- pt.df.w$thc.post - pt.df.w$thc.pre2
pt.df.w$BMI <- pt.df.w$demo_weight*703/(pt.df.w$demo_height)^2
pt.df.w$min_constriction_chg <- pt.df.w$min_constriction.post - pt.df.w$min_constriction.pre2
pt.df.w$AUC_chg <- pt.df.w$AUC.post - pt.df.w$AUC.pre2
pt.df.w$duration_chg <- pt.df.w$duration.post - pt.df.w$duration.pre2
pt.df.w$avg_end_percent_chg <- pt.df.w$avg_end_percent.post - pt.df.w$avg_end_percent.pre2
pt.df.w$slope_chg <- pt.df.w$slope.post - pt.df.w$slope.pre28.1 Add time from smoking to post test
## Need to work on; time formatted in Excel file.
testTimes <- read.xlsx(file.path(data_folder, ps_folder, "All SafetyScan Times_23Aug2022_revSG.xlsx"),
sheet = "Raw")
testTimes$pre_safetyscan_date_convert <- as.Date(testTimes$pre_safetyscan_date, origin = "1899-12-30")
testTimes$pre_safetyscan_time_convert <- as.POSIXct(paste0(testTimes$pre_safetyscan_date_convert, " ", testTimes$pre_safetyscan_time_hr, ":", testTimes$pre_safetyscan_time_min, ":", "00"), format = "%Y-%m-%d %H:%M:%S")
testTimes$consump_start_time_convert <- as.POSIXct(paste0(testTimes$pre_safetyscan_date_convert, " ", testTimes$consump_start_time_hr, ":", testTimes$consump_start_time_min, ":", "00"), format = "%Y-%m-%d %H:%M:%S")
testTimes$consump_end_time_convert <- as.POSIXct(paste0(testTimes$pre_safetyscan_date_convert, " ", testTimes$consump_end_time_hr, ":", testTimes$consump_end_time_min, ":", "00"), format = "%Y-%m-%d %H:%M:%S")
testTimes$post_safetyscan_time_convert <- as.POSIXct(paste0(testTimes$pre_safetyscan_date_convert, " ", testTimes$post_safetyscan_time_hr, ":", testTimes$post_safetyscan_time_min, ":", "00"), format = "%Y-%m-%d %H:%M:%S")
testTimes$postConsumpTimeToTest <- as.numeric(difftime(testTimes$post_safetyscan_time_convert,
testTimes$consump_end_time_convert,
units = "mins"))
testTimes$Post_PreTime <- as.numeric(difftime(testTimes$post_safetyscan_time_convert,
testTimes$pre_safetyscan_time_convert,
units = "mins"))
testTimes$remove <- ifelse(testTimes$subject_id == "001-056" & is.na(testTimes$postConsumpTimeToTest), 1, 0)
testTimes <- testTimes[testTimes$remove == 0, ]
# ## For subjects without a post Consumption to test time, we're using the time between pre and post test -- DO NOT "impute" this way -- JW 11/11/2022
# testTimes$postConsumpTimeToTest[is.na(testTimes$postConsumpTimeToTest)] <- testTimes$Post_PreTime[is.na(testTimes$postConsumpTimeToTest)]
pt.df <- merge(pt.df.w, testTimes[, c("subject_id", "postConsumpTimeToTest", "Post_PreTime")],
by = "subject_id")
by(pt.df$postConsumpTimeToTest, INDICES = list(pt.df$user_cat), summary)## : non-user
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## NA NA NA NaN NA NA 32
## ------------------------------------------------------------
## : occasional
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 54.00 60.50 64.00 64.19 67.00 84.00
## ------------------------------------------------------------
## : daily
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 53.00 58.00 60.00 61.15 65.00 74.00plot(pt.df$Post_PreTime, pt.df$postConsumpTimeToTest)
ps <- merge(ps, testTimes[, c("subject_id", "postConsumpTimeToTest", "Post_PreTime")],
by = "subject_id")
non_user.id <- pt.df$subject_id[pt.df$user_cat == "non-user"]
# View(testTimes[testTimes$subject_id %in% non_user.id, ])Add VAS data
vs <- read_sas(file.path(data_folder, ps_folder, "urban102.sas7bdat"))
vs <- vs[, c("subject_id", "period", "vas")]
attr(vs$subject_id, "label") <- NULL
attr(vs$subject_id, "format.sas") <- NULL
vas.post <- vs[vs$period == "POST", ]
vas.post$period <- NULL
names(vas.post)[names(vas.post) == "vas"] <- "vas.post"
vas.pre <- vs[vs$period == "PRE", ]
vas.pre$period <- NULL
names(vas.pre)[names(vas.pre) == "vas"] <- "vas.pre"
vas.w <- merge(vas.pre, vas.post, by = "subject_id")
vas.w$diff <- vas.w$vas.post - vas.w$vas.pre
summary(vas.w$diff)
## no difference between pre and post vas
vas.w <- vas.w[, c("subject_id", "vas.pre")]
names(vas.w)[names(vas.w) == "vas.pre"] <- "vas"
pt.df <- merge(pt.df, vas.w,
by = "subject_id",
all.x = T)
ps <- merge(ps, vas.w,
by = "subject_id",
all.x = T)vs <- read.xlsx(file.path(data_folder, ps_folder,
"All CDPHE VAS Scores_30Nov2022.xlsx"),
sheet = "VAS")
### Data Dictionary
# "subject_id"
# "vas0_high_score" -- vas score for how high you feel at pre
# "vas0_score_dc" -- how confident you feel about driving at pre
# "vas1_high_score" -- vas score for how high you feel at post
# "vas1_score_dc" -- how confident you feel about driving at post
pt.df <- merge (pt.df, vs,
by = "subject_id",
all.x = T)
pt.df$vas_high_score_chg <- pt.df$vas1_high_score - pt.df$vas0_high_score
pt.df$vas_score_dc_chg <- pt.df$vas1_score_dc - pt.df$vas0_score_dc8.2 Table 1
No Consumption end time recorded for non-users
# Demographics Table by User Category
table1(~ demo_age + demo_weight+ demo_height+BMI + demo_gender + thc_chg + postConsumpTimeToTest + Post_PreTime + min_constriction_chg + AUC_chg + duration_chg + slope_chg + avg_end_percent_chg + vas1_high_score + vas_high_score_chg + vas1_score_dc + vas_score_dc_chg|user_cat,
data = pt.df)| non-user (N=32) |
occasional (N=36) |
daily (N=33) |
Overall (N=101) |
|
|---|---|---|---|---|
| demo_age | ||||
| Mean (SD) | 32.9 (4.90) | 31.6 (4.98) | 33.5 (5.69) | 32.6 (5.21) |
| Median [Min, Max] | 33.5 [25.7, 42.2] | 30.1 [25.1, 41.9] | 32.6 [25.4, 45.3] | 32.3 [25.1, 45.3] |
| demo_weight | ||||
| Mean (SD) | 171 (48.5) | 173 (33.4) | 176 (33.1) | 173 (38.4) |
| Median [Min, Max] | 165 [85.0, 284] | 170 [105, 240] | 175 [113, 250] | 170 [85.0, 284] |
| demo_height | ||||
| Mean (SD) | 68.0 (4.83) | 69.6 (3.60) | 68.1 (3.52) | 68.6 (4.04) |
| Median [Min, Max] | 67.0 [60.0, 78.0] | 69.5 [61.0, 76.0] | 69.0 [59.0, 76.0] | 69.0 [59.0, 78.0] |
| BMI | ||||
| Mean (SD) | 25.5 (4.89) | 25.0 (4.02) | 26.7 (4.42) | 25.7 (4.46) |
| Median [Min, Max] | 24.5 [16.6, 34.2] | 24.5 [16.9, 32.6] | 25.8 [18.9, 34.9] | 25.1 [16.6, 34.9] |
| demo_gender | ||||
| Male | 13 (40.6%) | 23 (63.9%) | 18 (54.5%) | 54 (53.5%) |
| Female | 19 (59.4%) | 13 (36.1%) | 15 (45.5%) | 47 (46.5%) |
| thc_chg | ||||
| Mean (SD) | 0 (0) | 8.20 (9.71) | 29.9 (31.9) | 11.7 (21.9) |
| Median [Min, Max] | 0 [0, 0] | 5.73 [0, 46.6] | 17.8 [1.32, 124] | 3.93 [0, 124] |
| Missing | 2 (6.3%) | 7 (19.4%) | 8 (24.2%) | 17 (16.8%) |
| postConsumpTimeToTest | ||||
| Mean (SD) | NA (NA) | 64.2 (6.52) | 61.2 (4.87) | 62.7 (5.95) |
| Median [Min, Max] | NA [NA, NA] | 64.0 [54.0, 84.0] | 60.0 [53.0, 74.0] | 62.0 [53.0, 84.0] |
| Missing | 32 (100%) | 0 (0%) | 0 (0%) | 32 (31.7%) |
| Post_PreTime | ||||
| Mean (SD) | 111 (10.8) | 116 (9.20) | 116 (7.93) | 114 (9.54) |
| Median [Min, Max] | 109 [99.0, 148] | 113 [104, 147] | 117 [98.0, 137] | 112 [98.0, 148] |
| min_constriction_chg | ||||
| Mean (SD) | 6.22 (8.36) | 13.3 (10.5) | 11.4 (9.49) | 10.3 (9.87) |
| Median [Min, Max] | 7.35 [-14.6, 19.6] | 14.1 [-15.1, 34.4] | 9.92 [-1.72, 35.8] | 11.1 [-15.1, 35.8] |
| Missing | 3 (9.4%) | 6 (16.7%) | 8 (24.2%) | 17 (16.8%) |
| AUC_chg | ||||
| Mean (SD) | 3.06 (9.51) | 6.63 (7.82) | 7.67 (7.78) | 5.71 (8.56) |
| Median [Min, Max] | 4.21 [-21.6, 21.2] | 5.36 [-13.5, 23.5] | 5.46 [-1.82, 36.0] | 5.36 [-21.6, 36.0] |
| Missing | 3 (9.4%) | 6 (16.7%) | 8 (24.2%) | 17 (16.8%) |
| duration_chg | ||||
| Mean (SD) | -6.48 (58.0) | -16.4 (78.2) | -3.96 (42.9) | -9.29 (61.9) |
| Median [Min, Max] | -10.0 [-145, 152] | -8.00 [-219, 198] | -2.00 [-105, 108] | -7.00 [-219, 198] |
| Missing | 3 (9.4%) | 6 (16.7%) | 8 (24.2%) | 17 (16.8%) |
| slope_chg | ||||
| Mean (SD) | -0.0239 (0.0348) | -0.0398 (0.0384) | -0.0323 (0.0523) | -0.0321 (0.0420) |
| Median [Min, Max] | -0.0224 [-0.0959, 0.0727] | -0.0321 [-0.136, 0.0321] | -0.0259 [-0.176, 0.0372] | -0.0290 [-0.176, 0.0727] |
| Missing | 3 (9.4%) | 6 (16.7%) | 8 (24.2%) | 17 (16.8%) |
| avg_end_percent_chg | ||||
| Mean (SD) | 0.687 (9.11) | 0.836 (5.84) | 4.54 (9.08) | 1.89 (8.17) |
| Median [Min, Max] | 1.02 [-26.8, 20.7] | 0.627 [-14.6, 13.7] | 2.70 [-5.32, 42.3] | 1.30 [-26.8, 42.3] |
| Missing | 3 (9.4%) | 6 (16.7%) | 8 (24.2%) | 17 (16.8%) |
| vas1_high_score | ||||
| Mean (SD) | 0 (0) | 46.4 (17.4) | 47.0 (16.1) | 31.9 (25.8) |
| Median [Min, Max] | 0 [0, 0] | 48.5 [4.00, 79.0] | 46.0 [13.0, 81.0] | 37.0 [0, 81.0] |
| vas_high_score_chg | ||||
| Mean (SD) | 0 (0) | 45.6 (17.2) | 46.5 (16.4) | 31.4 (25.6) |
| Median [Min, Max] | 0 [0, 0] | 47.5 [4.00, 79.0] | 45.0 [13.0, 80.0] | 35.0 [0, 80.0] |
| vas1_score_dc | ||||
| Mean (SD) | 99.4 (1.85) | 42.5 (32.3) | 62.9 (32.5) | 67.2 (35.5) |
| Median [Min, Max] | 100 [90.0, 100] | 31.0 [0, 97.0] | 73.0 [0, 100] | 78.0 [0, 100] |
| vas_score_dc_chg | ||||
| Mean (SD) | 0.0781 (1.22) | -56.9 (32.4) | -36.7 (32.3) | -32.3 (35.5) |
| Median [Min, Max] | 0 [-3.00, 5.00] | -67.5 [-100, -3.00] | -27.0 [-100, 1.00] | -22.0 [-100, 5.00] |
8.2.1 Find potential mis-aligned data streams
I’m looking for any sharp declines over 10 frames after the 150th frame and then visualizing those trajectories to determine if there might be misalignment of the light test start frame.
ps$lagPctChg <- c(rep(0, 10), diff(ps$percent_change, lag = 10))
ps$lagID <- dplyr::lag(ps$subject_id, 10)
ps$lagtime <- dplyr::lag(ps$time, 10)
ps$lagEye <- dplyr::lag(ps$eye, 10)
ps$lagPctChg <- ifelse(ps$subject_id == ps$lagID & ps$time == ps$lagtime & ps$eye == ps$lagEye,
ps$lagPctChg, 0)
summary(ps$lagPctChg[ps$frame > 150])
hist(ps$lagPctChg[ps$frame > 150], breaks = 1000)
ps.150 <- ps[ps$frame > 150 & ps$eye == "Left", ]
pot.misaligned <- unique(ps.150[ps.150$lagPctChg <= -15, c("subject_id", "time", "user_type", "eye")])
pot.misaligned <- pot.misaligned[!(is.na(pot.misaligned$subject_id)), ]
pot.misaligned$misaligned <- 1
misaligned <- merge(ps, pot.misaligned,
by= c("subject_id", "time", "user_type", "eye"),
all.y = T)
mis.right <- misaligned[misaligned$eye == "Left", ]
misAlign.id <- unique(misaligned$subject_id)
for(i in misAlign.id){
print(ggplot(mis.right[mis.right$subject_id == i, ],
aes(x=frame, y = percent_change,
group = subject_id, color = i))+
geom_line()+
facet_grid(rows = vars(subject_id), cols = vars(tp))+
labs(title = paste0("Potential MisAligned Subject: ", i))+
theme_bw())
}
# jpeg(file.path(ps_folder, "Potential_Misaligned_001_109.jpg"),
# width = 12, height = 5, units = "in", res = 300)
# ggplot(mis.right[mis.right$subject_id == "001-109", ],
# aes(x=frame, y = percent_change, group = subject_id, color = subject_id))+
# geom_line()+
# facet_grid(rows = vars(subject_id), cols = vars(tp))+
# labs(title = "Potential MisAligned ")+
# theme_bw()
# dev.off()
# jpeg(file.path(ps_folder, "Potential_Misaligned_001_060.jpg"),
# width = 12, height = 5, units = "in", res = 300)
# ggplot(mis.right[mis.right$subject_id == "001-060", ],
# aes(x=frame, y = percent_change, group = subject_id, color = subject_id))+
# geom_line()+
# facet_grid(rows = vars(subject_id), cols = vars(tp))+
# labs(title = "Potential MisAligned ")+
# theme_bw()
# dev.off()
### New alignment view
# jpeg(file.path(ps_folder, "Potential_Misaligned_001_007.jpg"),
# width = 12, height = 5, units = "in", res = 300)
# ggplot(mis.right[mis.right$subject_id == "001-007", ],
# aes(x=frame, y = percent_change, group = subject_id, color = subject_id))+
# geom_line()+
# facet_grid(rows = vars(subject_id), cols = vars(tp))+
# labs(title = "Potential MisAligned: start at 2nd bump?")+
# theme_bw()
# dev.off()
#
# jpeg(file.path(ps_folder, "Potential_Misaligned_001_033.jpg"),
# width = 12, height = 5, units = "in", res = 300)
# ggplot(mis.right[mis.right$subject_id == "001-033", ],
# aes(x=frame, y = percent_change, group = subject_id, color = subject_id))+
# geom_line()+
# facet_grid(rows = vars(subject_id), cols = vars(tp))+
# labs(title = "Potential MisAligned: Odd pattern")+
# theme_bw()
# dev.off()
#
# jpeg(file.path(ps_folder, "Potential_Misaligned_001_038.jpg"),
# width = 12, height = 5, units = "in", res = 300)
# ggplot(mis.right[mis.right$subject_id == "001-038", ],
# aes(x=frame, y = percent_change, group = subject_id, color = subject_id))+
# geom_line()+
# facet_grid(rows = vars(subject_id), cols = vars(tp))+
# labs(title = "Potential MisAligned: Odd pattern")+
# theme_bw()
# dev.off()
#
# jpeg(file.path(ps_folder, "Potential_Misaligned_001_043.jpg"),
# width = 12, height = 5, units = "in", res = 300)
# ggplot(mis.right[mis.right$subject_id == "001-043", ],
# aes(x=frame, y = percent_change, group = subject_id, color = subject_id))+
# geom_line()+
# facet_grid(rows = vars(subject_id), cols = vars(tp))+
# labs(title = "Potential MisAligned: Both look odd, but mainly check pre; maybe review video")+
# theme_bw()
# dev.off()Remove Outliers
## Remove known outliers
ps$outliers <- 0
# ps$outliers[ps$subject_id == "001-109" & ps$tp == "pre2"] <- 1 # Ben revised
ps$outliers[ps$subject_id == "001-060" & ps$tp == "post"] <- 1
ps <- ps[ps$outliers == 0, ]8.3 Data Visualization
Plot of Pupil Size and Percent Change for “PRE” data by Eye and User Category
pre.df <- ps[ps$tp == "pre2", ]
ggplot(pre.df, aes(x=frame/30, y = pupil_size, group = subject_id))+
geom_line(alpha = 0.5, show.legend = FALSE)+
facet_grid(rows = vars(user_cat), cols = vars(eye))+
labs(title ="Pupil Size by Eye and THC use category",
x = "seconds")+
theme_bw()
ggplot(pre.df, aes(x=frame/30, y = percent_change, group = subject_id))+
geom_line(alpha = 0.5, show.legend = FALSE)+
facet_grid(rows = vars(user_cat), cols = vars(eye))+
labs(title = "Percent Change by Eye and THC use category",
x = "seconds")+
theme_bw()
Plot of PRE/POST for Right Eye
right.df <- ps[ps$eye == "Right", ]
ggplot(right.df, aes(x=frame/30, y = pupil_size, group = subject_id))+
geom_line(show.legend = FALSE, alpha = 0.5)+
facet_grid(rows = vars(user_cat), cols = vars(tp))+
labs(title ="Pupil Size by Pre/Post and THC use category",
x = "seconds")+
theme_bw()
# jpeg(file.path(res_folder, "PrePost_Right_PercentChange.jpg"),
# height = 6, width = 8, res = 300, units = "in")
ggplot(right.df, aes(x=frame/30, y = percent_change, group = subject_id))+
geom_line(show.legend = FALSE, alpha = 0.5)+
facet_grid(rows = vars(user_cat), cols = vars(tp))+
labs(title = "Percent Change by Pre/Post and THC use category",
x = "seconds")+
theme_bw()
# dev.off()Plots of Left and Right Eye for “POST” data. One pt has negative values for pupil size.
post.df <- ps[ps$tp == "post", ]
ggplot(post.df, aes(x=frame/30, y = percent_change, group = subject_id))+
geom_line(show.legend = FALSE, alpha = 0.5)+
facet_grid(rows = vars(user_cat), cols = vars(eye))+
labs(title = "Percent Change by Eye and THC use category - Post",
x = "seconds")+
theme_bw()
pre.df <- ps[ps$tp == "pre2", ]
# jpeg(file.path(res_folder, "LeftRight_Pre_PercentChange.jpg"),
# height = 6, width = 8, res = 300, units = "in")
ggplot(pre.df, aes(x=frame/30, y = percent_change, group = subject_id))+
geom_line(show.legend = FALSE, alpha = 0.5)+
facet_grid(rows = vars(user_cat), cols = vars(eye))+
labs(title = "Percent Change by Eye and THC use category - Pre",
x = "seconds")+
theme_bw()
# dev.off()9 Exploratory Analysis
9.1 Over all subjects and timepoints
9.1.1 Mean Function
Plot the Mean and +/- 1 SD functions for the percent change in pupil light reflex data for the pre and post data by THC user category. (Added code to input frame numbers when NA).
right_0.df <- right.df[right.df$frame >= 0, c("subject_id", "tp", "user_cat",
"frame", "percent_change")]
right_0.df <- right_0.df[order(right_0.df$subject_id, right_0.df$tp, right_0.df$frame), ]
right_0.w <- reshape(right_0.df,
timevar = "frame",
idvar = c("subject_id", "tp", "user_cat"),
direction = "wide")
rownames(right_0.w) <- paste0(right_0.w$subject_id, "_", right_0.w$tp)
pct_chg <- names(right_0.w[, 4:602])
mean_fxn <- as.data.frame(aggregate(right_0.w[, 4:602],
list(right_0.w$tp,
right_0.w$user_cat),
FUN = function(x) mean(x, na.rm = T)))
mean_fxn.l <- reshape(mean_fxn,
varying = pct_chg,
v.names = "percent_change",
timevar = "frame",
times = pct_chg,
direction = "long")
mean_fxn.l$frame <- as.numeric(gsub("percent_change.", "", mean_fxn.l$frame))
rownames(mean_fxn.l) <- NULL
mean_fxn.l$id <- NULL
names(mean_fxn.l)[names(mean_fxn.l) == "Group.1"] <- "tp"
names(mean_fxn.l)[names(mean_fxn.l) == "Group.2"] <- "user_cat"
mean_fxn.l$grp <- paste0(mean_fxn.l$tp, "_", mean_fxn.l$user_cat)
ggplot(mean_fxn.l, aes(x=frame/30, y = percent_change, group = grp,
color = user_cat))+
geom_line()+
facet_grid(rows = vars(tp))+
labs(title = "Average Percent Change by Pre/Post and THC use category",
x = "seconds")+
theme_bw()## Warning: Removed 449 row(s) containing missing values (geom_path).
sd_fxn <- as.data.frame(aggregate(right_0.w[, 4:602],
list(right_0.w$tp,
right_0.w$user_cat),
FUN = function(x) sd(x, na.rm = T)))NA’s are when there’s no data in for that time point and user category. Min frame value for NA is 480.
9.1.2 fPCA all subjects and timepoints
Truncating to frame 400
right_0.fpca <- fpca.face(Y = as.matrix(right_0.w[, 4:404]), pve = 0.95, knots = 30, var = T)
# str(right_0.fpca)
# plot_shiny(right_0.fpca)
right_0.fpca.pve <- round(right_0.fpca$evalues/sum(right_0.fpca$evalues)*100, 2)
# right_0.fpca.pve
mu <- right_0.fpca$mu
right_sd <- sqrt(right_0.fpca$evalues)
right_Phi <- right_0.fpca$efunctions
df_plot <- data.frame(seconds = seq(0, 400, by = 1)/30,
mu = mu,
errband1 = 2*right_Phi[, 1]*right_sd[1],
errband2 = 2*right_Phi[, 2]*right_sd[2],
errband3 = 2*right_Phi[, 3]*right_sd[3],
errband4 = 2*right_Phi[, 4]*right_sd[4],
errband5 = 2*right_Phi[, 5]*right_sd[5]
)
ylim_max = max(df_plot$mu) + max(df_plot [, c("errband1", "errband2","errband3","errband4","errband5")], na.rm = T)
ylim_min = min(df_plot$mu) - max(df_plot [, c("errband1", "errband2","errband3","errband4","errband5")], na.rm = T)
colors <- c("Pop.Mean" = "black", "+2SD" = "red", "-2SD" = "blue")
plot_pc1 <- ggplot(data = df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband1, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband1, color = "-2SD"))+
labs(x = "seconds",
y = "Percent Change",
color = "Legend",
title = paste("PC1:", "% Var Explained:", right_0.fpca.pve[1]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(ylim_min, ylim_max))+
theme_bw()
plot_pc2 <- ggplot(data = df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband2, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband2, color = "-2SD"))+
labs(x = "seconds",
y = "Percent Change",
color = "Legend",
title = paste("PC2:", "% Var Explained:", right_0.fpca.pve[2]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(ylim_min, ylim_max))+
theme_bw()
plot_pc3 <- ggplot(data = df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband3, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband3, color = "-2SD"))+
labs(x = "seconds",
y = "Percent Change",
color = "Legend",
title = paste("PC3:", "% Var Explained:", right_0.fpca.pve[3]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(ylim_min, ylim_max))+
theme_bw()
plot_pc4 <- ggplot(data = df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband4, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband4, color = "-2SD"))+
labs(x = "seconds",
y = "Percent Change",
color = "Legend",
title = paste("PC4:", "% Var Explained:", right_0.fpca.pve[4]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(ylim_min, ylim_max))+
theme_bw()
plot_pc5 <- ggplot(data = df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband5, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband5, color = "-2SD"))+
labs(x = "seconds",
y = "Percent Change",
color = "Legend",
title = paste("PC5:", "% Var Explained:", right_0.fpca.pve[5]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(ylim_min, ylim_max))+
theme_bw()
legend_postPC <- g_legend(plot_pc1)
blank <- grid.rect(gp=gpar(col="white"))
grid.arrange(plot_pc1+theme(legend.position = "none"),
plot_pc2+theme(legend.position = "none"),
plot_pc3+theme(legend.position = "none"),
plot_pc4+theme(legend.position = "none"),
plot_pc5+theme(legend.position = "none"),
blank,
legend_postPC,
layout_matrix = rbind(c(1,2,3,7), c(4,5, 6 , 7)),
widths = c(10, 10, 10, 3),
nrow = 2, ncol = 4)
# jpeg(file.path(res_folder, "Post_PC4.jpg"),
# height = 6, width = 8, res = 300, units = "in")
grid.arrange(plot_pc1+theme(legend.position = "none"),
plot_pc2+theme(legend.position = "none"),
plot_pc3+theme(legend.position = "none"),
plot_pc4+theme(legend.position = "none"),
legend_postPC,
layout_matrix = rbind(c(1,2,5), c(3,4,5)),
widths = c(10, 10, 3),
nrow = 2, ncol = 3)
# dev.off()PC1 plot: Individuals with low loading on PC1 (-2SD) have less constriction than the average and individuals with a high loading (+2SD) have more constriction. Rebound effect?
PC2 plot: Overall shape of trajectory & pattern of recovery
Plot individuals with high/low PC2 overall scores.
right_scores <- right_0.fpca$scores
q.95 <- quantile(right_scores[, 2], p = 0.95)
highPC2 <- rownames(right_scores)[right_scores[, 2] > q.95]
highPC2.df <- data.frame(subject_id = substr(highPC2, 1, 7),
tp = substr(highPC2, 9,12))
for(i in 1:nrow(highPC2.df)){
plot.df <- right_0.df[right_0.df$subject_id == highPC2.df$subject_id[i] & right_0.df$tp == highPC2.df$tp[i], ]
print(ggplot(plot.df, aes(x=frame30, y = percent_change, group = subject_id, color = subject_id))+
geom_line(show.legend = FALSE)+
labs(title = paste("Percent Change for high PC2 score --", "Subject:",
highPC2.df$subject_id[i], "Timepoint:", highPC2.df$tp[i]),
x = "seconds")+
theme_bw())
}q.05 <- quantile(right_scores[, 2], p = 0.05)
lowPC2 <- rownames(right_scores)[right_scores[, 2] < q.05]
lowPC2.df <- data.frame(subject_id = substr(lowPC2, 1, 7),
tp = substr(lowPC2, 9,12))
for(i in 1:nrow(lowPC2.df)){
plot.df <- right_0.df[right_0.df$subject_id == lowPC2.df$subject_id[i] & right_0.df$tp == lowPC2.df$tp[i], ]
print(ggplot(plot.df, aes(x=frame/30, y = percent_change, group = subject_id, color = subject_id))+
geom_line(show.legend = FALSE)+
labs(title = paste("Percent Change for low PC2 score --", "Subject:", lowPC2.df$subject_id[i], "Timepoint:", lowPC2.df$tp[i]),
x = "seconds")+
theme_bw())
}9.2 Create Analytic Sample
Make sure datasets have the same participants and are truncated at frame 400
ps <- ps[ps$frame >= 0 & ps$frame <= 400,
c("subject_id", "tp", "eye", "frame", "percent_change")]
ps <- ps[order(ps$subject_id, ps$tp, ps$eye, ps$frame), ]
ps$frame_char <- str_pad(ps$frame, width = 3, side = c("left"), pad = "0")
ps$frame <- NULL
ps.w <- reshape(ps[, c("subject_id","tp", "eye", "frame_char", "percent_change")],
timevar = "frame_char",
idvar = c("subject_id", "tp", "eye"),
direction = "wide")
var.names <- c(names(ps.w)[1:3], sort(names(ps.w)[4:404]))
ps.w <- ps.w[, var.names]
id.names <- paste(ps.w$subject_id, ps.w$tp, ps.w$eye, sep = "_")
rownames(ps.w) <- id.names
missData <- apply(ps.w[, 4:404], MARGIN = 1, FUN = function(x) sum(is.na(x)))
missInterpolate <- function(x){
z <- zoo(x, order.by = seq(0:400))
y <- na.approx(z, maxgap = 3, na.rm = FALSE)
return(as.numeric(y))
}
test <- apply(ps.w[, 4:404],
MARGIN = 1,
FUN = missInterpolate)
ps.w <- data.frame(t(test))
names(ps.w) <- var.names[4:404]
ps.w$subject_id <- substr(rownames(ps.w), 1, 7)
ps.w$tp <- ifelse(grepl("pre2", rownames(ps.w)) == T, "pre2", "post")
ps.w$eye <- ifelse(grepl("Left", rownames(ps.w)) == T, "Left", "Right")
ps.w <- ps.w[, var.names]
pctChg.names <- var.names[4:404]
ps <- reshape(ps.w,
varying = pctChg.names,
v.names = "percent_change",
timevar = "frame",
times = 0:400,
idvar = c("subject_id", "tp", "eye"),
direction = "long")
ps <- ps[order(ps$subject_id, ps$tp, ps$eye), ]right_0.post <- ps[ps$tp == "post" & ps$eye == "Right", ]
post.ids <- unique(right_0.post$subject_id)
right_0.post.w <- ps.w[ps.w$tp == "post" & ps.w$eye == "Right", ]
right_0.pt <- reshape(ps[ps$eye == "Right", ],
timevar = "tp",
idvar = c("subject_id", "frame"),
direction = "wide")
right_0.pt$wiPctChg <- right_0.pt$percent_change.post - right_0.pt$percent_change.pre2
right_0.pt <- right_0.pt[, c("subject_id", "frame", "wiPctChg")]
right_0.pt.w <- reshape(right_0.pt[, c("subject_id", "frame", "wiPctChg")],
timevar = "frame",
idvar = "subject_id",
direction = "wide")
# remove rows where all data is missing (e.g. pt didn't have both pre and post)
allMissing <- rowSums(is.na(right_0.pt.w[, 2:402]))
rowsAllMissing <- names(allMissing)[allMissing == 401]
right_0.pt.w <- right_0.pt.w[!(rownames(right_0.pt.w) %in% rowsAllMissing), ]
################################# ----------------------------- Left eye
left_0.post <- ps[ps$tp == "post" & ps$eye == "Left", ]
left.post.ids <- unique(left_0.post$subject_id)
left_0.post.w <- ps.w[ps.w$tp == "post" & ps.w$eye == "Left", ]
left_0.pt <- reshape(ps[ps$eye == "Left", ],
timevar = "tp",
idvar = c("subject_id", "frame"),
direction = "wide")
left_0.pt$wiPctChg <- left_0.pt$percent_change.post - left_0.pt$percent_change.pre2
left_0.pt <- left_0.pt[, c("subject_id", "frame", "wiPctChg")]
left_0.pt.w <- reshape(left_0.pt[, c("subject_id", "frame", "wiPctChg")],
timevar = "frame",
idvar = "subject_id",
direction = "wide")
# remove rows where all data is missing (e.g. pt didn't have both pre and post)
allMissing <- rowSums(is.na(left_0.pt.w[, 2:402]))
rowsAllMissing <- names(allMissing)[allMissing == 401]
left_0.pt.w <- left_0.pt.w[!(rownames(left_0.pt.w) %in% rowsAllMissing), ]
## Find intersection of all participant files across data sets to define the analytic subjects
analytic.N <- intersect(unique(right_0.post$subject_id), unique(right_0.pt$subject_id))
analytic.N <- intersect(analytic.N, unique(right_0.post.w$subject_id))
analytic.N <- intersect(analytic.N, unique(right_0.pt.w$subject_id))
analytic_L.N <- intersect(unique(left_0.post.w$subject_id), unique(left_0.post$subject_id))
analytic_L.N <- intersect(analytic.N, unique(left_0.pt$subject_id))
analytic_L.N <- intersect(analytic.N, unique(left_0.pt.w$subject_id))
analytic_LR.N <- intersect(analytic.N, analytic_L.N)
## Filter all datasets to analytic sample
left_0.post <- left_0.post[left_0.post$subject_id %in% analytic_LR.N, ]
left_0.post$seconds <- left_0.post$frame/30
left_0.post.w <- left_0.post.w[left_0.post.w$subject_id %in% analytic_LR.N ,]
row.names(left_0.post.w) <- left_0.post.w$subject_id
left_0.post <- merge(left_0.post, pt.df[, c("subject_id", "user_cat")],
by = "subject_id",
all.x = T)
left_0.post.w <- merge(left_0.post.w, pt.df[, c("subject_id", "user_cat")],
by = "subject_id",
all.x = T)
rownames(left_0.post.w) <- left_0.post.w$subject_id
left_0.pt <- left_0.pt[left_0.pt$subject_id %in% analytic_LR.N, ]
left_0.pt$seconds <- left_0.pt$frame/30
left_0.pt.w <- left_0.pt.w[left_0.pt.w$subject_id %in% analytic_LR.N, ]
row.names(left_0.pt.w) <- left_0.pt.w$subject_id
left_0.pt <- merge(left_0.pt, pt.df[, c("subject_id", "user_cat")],
by = "subject_id",
all.x = T)
left_0.pt.w <- merge(left_0.pt.w, pt.df[, c("subject_id", "user_cat")],
by = "subject_id",
all.x = T)
rownames(left_0.pt.w) <- left_0.pt.w$subject_id
## Filter all datasets to analytic sample
right_0.post <- right_0.post[right_0.post$subject_id %in% analytic.N, ]
right_0.post$seconds <- right_0.post$frame/30
right_0.post.w <- right_0.post.w[right_0.post.w$subject_id %in% analytic.N ,]
row.names(right_0.post.w) <- right_0.post.w$subject_id
right_0.post <- merge(right_0.post, pt.df[, c("subject_id", "user_cat")],
by = "subject_id",
all.x = T)
right_0.post.w <- merge(right_0.post.w, pt.df[, c("subject_id", "user_cat")],
by = "subject_id",
all.x = T)
rownames(right_0.post.w) <- right_0.post.w$subject_id
right_0.pt <- right_0.pt[right_0.pt$subject_id %in% analytic.N, ]
right_0.pt$seconds <- right_0.pt$frame/30
right_0.pt.w <- right_0.pt.w[right_0.pt.w$subject_id %in% analytic.N, ]
right_0.pt <- merge(right_0.pt, pt.df[, c("subject_id", "user_cat")],
by = "subject_id",
all.x = T)
right_0.pt.w <- merge(right_0.pt.w, pt.df[, c("subject_id", "user_cat")],
by = "subject_id",
all.x = T)
rownames(right_0.pt.w) <- right_0.pt.w$subject_idSend Merged dataset to Dr. Wrobel (20221128)
right_0.post <- merge(right_0.post, pt.df,
by = "subject_id")
right_0.pt <- merge(right_0.pt, pt.df,
by = "subject_id")
saveRDS(right_0.post, file.path(data_folder, ps_folder,
"PupilLightReflex_POST_rightEye_20221128.rds"))
saveRDS(right_0.pt, file.path(data_folder, ps_folder,
"PupilLightReflex_WPD_rightEye_20221128.rds"))
saveRDS(pt.df[pt.df$subject_id %in% analytic.N,], file.path(data_folder, ps_folder,
"PupilLightReflex_PtData_20221128.rds"))No Consumption end time recorded for non-users
# Demographics Table by User Category
table1(~demo_age + demo_weight+ demo_height+BMI + demo_gender + thc_chg + postConsumpTimeToTest + Post_PreTime + min_constriction_chg + AUC_chg + duration_chg + slope_chg + avg_end_percent_chg +vas1_high_score + vas_high_score_chg + vas1_score_dc + vas_score_dc_chg|user_cat,
data = pt.df[pt.df$subject_id %in% analytic.N, ])| non-user (N=29) |
occasional (N=30) |
daily (N=25) |
Overall (N=84) |
|
|---|---|---|---|---|
| demo_age | ||||
| Mean (SD) | 32.3 (4.70) | 31.1 (4.75) | 32.8 (5.71) | 32.0 (5.02) |
| Median [Min, Max] | 31.1 [25.7, 42.2] | 29.7 [25.1, 41.9] | 32.0 [25.4, 45.3] | 31.1 [25.1, 45.3] |
| demo_weight | ||||
| Mean (SD) | 167 (48.8) | 169 (34.0) | 180 (29.8) | 172 (38.7) |
| Median [Min, Max] | 162 [85.0, 284] | 165 [105, 240] | 175 [125, 250] | 169 [85.0, 284] |
| demo_height | ||||
| Mean (SD) | 68.0 (4.97) | 69.5 (3.84) | 68.5 (3.40) | 68.7 (4.16) |
| Median [Min, Max] | 67.0 [60.0, 78.0] | 69.5 [61.0, 76.0] | 69.0 [63.0, 76.0] | 69.0 [60.0, 78.0] |
| BMI | ||||
| Mean (SD) | 24.9 (4.72) | 24.5 (3.96) | 27.1 (4.26) | 25.4 (4.41) |
| Median [Min, Max] | 23.9 [16.6, 34.1] | 24.3 [16.9, 32.5] | 26.8 [19.0, 34.9] | 24.7 [16.6, 34.9] |
| demo_gender | ||||
| Male | 13 (44.8%) | 20 (66.7%) | 16 (64.0%) | 49 (58.3%) |
| Female | 16 (55.2%) | 10 (33.3%) | 9 (36.0%) | 35 (41.7%) |
| thc_chg | ||||
| Mean (SD) | 0 (0) | 8.20 (9.71) | 29.9 (31.9) | 11.9 (22.0) |
| Median [Min, Max] | 0 [0, 0] | 5.73 [0, 46.6] | 17.8 [1.32, 124] | 3.96 [0, 124] |
| Missing | 0 (0%) | 1 (3.3%) | 0 (0%) | 1 (1.2%) |
| postConsumpTimeToTest | ||||
| Mean (SD) | NA (NA) | 63.9 (6.26) | 60.2 (3.78) | 62.2 (5.57) |
| Median [Min, Max] | NA [NA, NA] | 63.5 [54.0, 84.0] | 60.0 [53.0, 67.0] | 62.0 [53.0, 84.0] |
| Missing | 29 (100%) | 0 (0%) | 0 (0%) | 29 (34.5%) |
| Post_PreTime | ||||
| Mean (SD) | 110 (8.90) | 116 (9.72) | 116 (6.58) | 114 (8.97) |
| Median [Min, Max] | 109 [99.0, 131] | 114 [104, 147] | 117 [106, 137] | 112 [99.0, 147] |
| min_constriction_chg | ||||
| Mean (SD) | 6.22 (8.36) | 13.3 (10.5) | 11.4 (9.49) | 10.3 (9.87) |
| Median [Min, Max] | 7.35 [-14.6, 19.6] | 14.1 [-15.1, 34.4] | 9.92 [-1.72, 35.8] | 11.1 [-15.1, 35.8] |
| AUC_chg | ||||
| Mean (SD) | 3.06 (9.51) | 6.63 (7.82) | 7.67 (7.78) | 5.71 (8.56) |
| Median [Min, Max] | 4.21 [-21.6, 21.2] | 5.36 [-13.5, 23.5] | 5.46 [-1.82, 36.0] | 5.36 [-21.6, 36.0] |
| duration_chg | ||||
| Mean (SD) | -6.48 (58.0) | -16.4 (78.2) | -3.96 (42.9) | -9.29 (61.9) |
| Median [Min, Max] | -10.0 [-145, 152] | -8.00 [-219, 198] | -2.00 [-105, 108] | -7.00 [-219, 198] |
| slope_chg | ||||
| Mean (SD) | -0.0239 (0.0348) | -0.0398 (0.0384) | -0.0323 (0.0523) | -0.0321 (0.0420) |
| Median [Min, Max] | -0.0224 [-0.0959, 0.0727] | -0.0321 [-0.136, 0.0321] | -0.0259 [-0.176, 0.0372] | -0.0290 [-0.176, 0.0727] |
| avg_end_percent_chg | ||||
| Mean (SD) | 0.687 (9.11) | 0.836 (5.84) | 4.54 (9.08) | 1.89 (8.17) |
| Median [Min, Max] | 1.02 [-26.8, 20.7] | 0.627 [-14.6, 13.7] | 2.70 [-5.32, 42.3] | 1.30 [-26.8, 42.3] |
| vas1_high_score | ||||
| Mean (SD) | 0 (0) | 48.2 (17.8) | 47.2 (14.7) | 31.3 (26.4) |
| Median [Min, Max] | 0 [0, 0] | 51.3 [4.00, 79.0] | 46.0 [13.0, 78.0] | 37.5 [0, 79.0] |
| vas_high_score_chg | ||||
| Mean (SD) | 0 (0) | 47.6 (17.5) | 47.0 (14.7) | 31.0 (26.1) |
| Median [Min, Max] | 0 [0, 0] | 50.0 [4.00, 79.0] | 45.0 [13.0, 77.0] | 37.5 [0, 79.0] |
| vas1_score_dc | ||||
| Mean (SD) | 99.4 (1.93) | 43.7 (32.3) | 62.0 (31.9) | 68.4 (35.1) |
| Median [Min, Max] | 100 [90.0, 100] | 34.5 [0, 97.0] | 73.0 [0, 100] | 79.0 [0, 100] |
| vas_score_dc_chg | ||||
| Mean (SD) | -0.0517 (1.17) | -55.6 (32.4) | -37.6 (31.7) | -31.1 (35.0) |
| Median [Min, Max] | 0 [-3.00, 5.00] | -65.5 [-100, -3.00] | -27.0 [-100, 1.00] | -21.0 [-100, 5.00] |
9.3 Post Data
post.user <- ggplot(right_0.post, aes(x=seconds, y = percent_change, group = subject_id))+
geom_line(show.legend = FALSE, alpha = 0.5)+
facet_grid(rows = vars(user_cat))+
labs(title ="POST data percent change by THC use category")+
theme_bw()9.4 Within Subject
9.4.1 Mean function
Calculate the difference (post-pre) within subjects and plot by THC user category
wi.user <- ggplot(right_0.pt, aes(x=seconds, y = wiPctChg, group = subject_id))+
geom_line(show.legend = FALSE, alpha = 0.5)+
facet_grid(rows = vars(user_cat))+
labs(title ="Within subject difference in percent change by THC use category")+
theme_bw()
# jpeg(file.path(res_folder, "Post_WPD_Trajectories.jpg"),
# height = 5, width = 12, res = 300, units = "in")
grid.arrange(post.user, wi.user, nrow = 1)
# dev.off()Non-user plot seems “centered” around 0 but still showing heterogeneity. Lots of heterogeneity across user-groups, but a mostly positive difference.
9.5 Plot the mean and +/- 1 SD functions for within subject different in percent change
mean_fxn.pt <- as.data.frame(aggregate(right_0.pt.w[, 2:402],
list(right_0.pt.w$user_cat),
FUN = function(x) mean(x, na.rm = T)))
wiPctChg <- names(right_0.pt.w)[2:402]
mean_fxn.pt.l <- reshape(mean_fxn.pt,
varying = wiPctChg,
v.names = "wi_percent_change",
timevar = "frame",
times = wiPctChg,
direction = "long")
mean_fxn.pt.l$frame <- as.numeric(gsub("wiPctChg.", "", mean_fxn.pt.l$frame))
rownames(mean_fxn.pt.l) <- NULL
mean_fxn.pt.l$id <- NULL
names(mean_fxn.pt.l)[names(mean_fxn.pt.l) == "Group.1"] <- "user_cat"
ggplot(mean_fxn.pt.l, aes(x=frame/30, y = wi_percent_change, group = user_cat, color = user_cat))+
geom_line()+
labs(title ="Average Within subject difference in percent change by THC use category",
x = "seconds")+
theme_bw()
Difference in trajectories (post-pre), show a distinct difference between non-user and both user groups (up to frame 200), but the differences might not be significant. Harder to distinguish between the occasional and daily user trajectories.
# Check to make sure there are significant numbers in each user category
table(right_0.pt.w$user_cat)##
## non-user occasional daily
## 29 30 25sd_fxn.pt <- as.data.frame(aggregate(right_0.pt.w[, 2:402],
list(right_0.pt.w$user_cat),
FUN = function(x) sd(x, na.rm = T)))
wiPctChg <- names(right_0.pt.w)[2:402]
sd_fxn.pt.l <- reshape(sd_fxn.pt,
varying = wiPctChg,
v.names = "wi_percent_change",
timevar = "frame",
times = wiPctChg,
direction = "long")
sd_fxn.pt.l$frame <- as.numeric(gsub("wiPctChg.", "", sd_fxn.pt.l$frame))
rownames(sd_fxn.pt.l) <- NULL
sd_fxn.pt.l$id <- NULL
names(sd_fxn.pt.l)[names(sd_fxn.pt.l) == "Group.1"] <- "user_cat"
sd_fxn.pt.l$wi_percent_change_neg <- -1*sd_fxn.pt.l$wi_percent_change
ggplot(sd_fxn.pt.l, aes(x=frame/30, y = wi_percent_change, group = user_cat, color = user_cat))+
geom_line()+
geom_line(aes(x=frame/30, y = wi_percent_change_neg, group = user_cat, color = user_cat),
linetype = "dashed")+
labs(title ="+/- 1 SD Within subject difference in percent change by THC use category",
x = "seconds")+
theme_bw()
9.6 Missing Data for Within Subject Differences
## number of missing data points in each column
missingnessCol <- apply(right_0.pt.w[, 2:402],
MARGIN = 2,
FUN = function(x) sum(is.na(x)))
sum(missingnessCol == 0) ## 140 columns without any missing data## [1] 223sum(missingnessCol == 84) ## 109 columns with all missing data## [1] 0sum(missingnessCol <= 68) ## 438 columns where at least 80% of participants have data## [1] 401hist(missingnessCol, breaks = 30)
missing.df <- data.frame(frame = seq(0, 400, by =1),
missingness = round(missingnessCol/nrow(right_0.pt.w)*100, 2),
stringsAsFactors = F)
ggplot(missing.df, aes(x=frame/30, y= missingness))+
geom_line()
Truncate within person difference data to frame 400 where missingness
reaches 50%.
Try to visualize missing data in within person data frame
wi_pct_chg <- names(right_0.pt.w)[2:402]
right_0.pt.l <- reshape(right_0.pt.w,
varying = wi_pct_chg,
v.names = "wi_pct_chg_diff",
timevar = 'frame',
times = wi_pct_chg,
direction = "long")
right_0.pt.l$frame <- as.numeric(gsub("wiPctChg.", "", right_0.pt.l$frame))
rownames(right_0.pt.l) <- NULL
right_0.pt.l$id <- NULL
right_0.pt.l$notMissing <- ifelse(is.na(right_0.pt.l$wi_pct_chg_diff), 0, 1)
ggplot(right_0.pt.l, aes(x = as.factor(frame), y = subject_id, fill = as.factor(notMissing)))+
geom_raster(alpha = 0.8)+
scale_fill_manual(values = c("black", 'white'),
labels = c("Missing", "Present"))+
scale_x_discrete(breaks = seq(0, 400, by = 25))+
geom_vline(xintercept = 350,color = "darkred")+
geom_vline(xintercept = 375,color = "darkblue")+
theme(axis.text.x=element_text(angle = 45, vjust = 1, hjust = 1))
9.6.1 fPCA Within Person Differences
right_0_wi.fpca <- fpca.face(Y = as.matrix(right_0.pt.w[, 2:402]), pve = 0.95, knots = 30, var = T)
# str(right_0.fpca)
# plot_shiny(right_0.fpca)
right_0_wi.fpca.pve <- round(right_0_wi.fpca$evalues/sum(right_0_wi.fpca$evalues)*100, 2)
mu <- right_0_wi.fpca$mu
right_sd <- sqrt(right_0_wi.fpca$evalues)
right_Phi <- right_0_wi.fpca$efunctions
wi.df_plot <- data.frame(seconds = seq(0, 400, by = 1)/30,
mu = mu,
errband1 = 2*right_Phi[, 1]*right_sd[1],
errband2 = 2*right_Phi[, 2]*right_sd[2],
errband3 = 2*right_Phi[, 3]*right_sd[3],
errband4 = 2*right_Phi[, 4]*right_sd[4],
errband5 = 2*right_Phi[, 5]*right_sd[5],
errband6 = 2*right_Phi[, 6]*right_sd[6])
wi.ylim_max = max(wi.df_plot$mu) + max(wi.df_plot [, c("errband1", "errband2","errband3","errband4","errband5", "errband6")], na.rm = T)
wi.ylim_min = min(wi.df_plot$mu) - max(wi.df_plot [, c("errband1", "errband2","errband3","errband4","errband5", "errband6")], na.rm = T)
colors <- c("Pop.Mean" = "black", "+2SD" = "red", "-2SD" = "blue")
wi.plot_pc1 <- ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband1, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband1, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = paste("PC1:", "% Var Explained:", right_0_wi.fpca.pve[1]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(wi.ylim_min, wi.ylim_max))+
theme_bw()
wi.plot_pc2 <- ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband2, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband2, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = paste("PC2:", "% Var Explained:", right_0_wi.fpca.pve[2]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(wi.ylim_min, wi.ylim_max))+
theme_bw()
wi.plot_pc3 <- ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband3, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband3, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = paste("PC3:", "% Var Explained:", right_0_wi.fpca.pve[3]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(wi.ylim_min, wi.ylim_max))+
theme_bw()
wi.plot_pc4 <- ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband4, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband4, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = paste("PC4:", "% Var Explained:", right_0_wi.fpca.pve[4]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(wi.ylim_min, wi.ylim_max))+
theme_bw()
wi.plot_pc5 <- ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband5, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband5, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = paste("PC5:", "% Var Explained:", right_0_wi.fpca.pve[5]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(wi.ylim_min, wi.ylim_max))+
theme_bw()
wi.plot_pc6 <- ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband6, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband6, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = paste("PC6:", "% Var Explained:", right_0_wi.fpca.pve[6]))+
scale_color_manual(values = colors)+
scale_y_continuous(limits = c(wi.ylim_min, wi.ylim_max))+
theme_bw()
legend_wiPC <- g_legend(wi.plot_pc1)
grid.arrange(wi.plot_pc1+theme(legend.position = "none"),
wi.plot_pc2+theme(legend.position = "none"),
wi.plot_pc3+theme(legend.position = "none"),
wi.plot_pc4+theme(legend.position = "none"),
wi.plot_pc5+theme(legend.position = "none"),
wi.plot_pc6+theme(legend.position = "none"),
legend_wiPC,
layout_matrix = rbind(c(1,2,3, 7), c(4, 5, 6, 7)),
widths = c(10, 10, 10, 3),
nrow = 2, ncol = 4)
# jpeg(file.path(res_folder, "WPD_PC4.jpg"),
# height = 6, width = 8, res = 300, units = "in")
grid.arrange(wi.plot_pc1+theme(legend.position = "none"),
wi.plot_pc2+theme(legend.position = "none"),
wi.plot_pc3+theme(legend.position = "none"),
wi.plot_pc4+theme(legend.position = "none"),
legend_wiPC,
layout_matrix = rbind(c(1,2,5), c(3,4,5)),
widths = c(10, 10, 3),
nrow = 2, ncol = 3)
# dev.off()PC1: Individuals with high scores on PC1 have a weaker minimal constriction at post compared to pre. Individuals with low scores on PC1 have a stronger minimal constriction at post compared to pre.
PC2: Individuals with high loading on PC2 have a weaker initial constriction at post and a stronger rebound dilation past the 200th frame. Individuals with a low loading on PC2 have a stronger initial constriction at paost and a weaker rebound dilation past the 200th frame.
wi.scores <- as.data.frame(right_0_wi.fpca$scores)
names(wi.scores) <- c("PC1", "PC2", "PC3", "PC4", "PC5", "PC6")
wi.scores$subject_id <- rownames(wi.scores)
pt.wi.scores <- merge(wi.scores, pt.df,
by = "subject_id",
all.x = T)
pt.wi.scores$smoker <- ifelse(pt.wi.scores$user_cat %in% c("daily", "occasional"),1, 0)# pc_ind_plots <- function(scores.df, raw.df, pc_plot.df, q, pc= "PC1", id = "subject_id", pc.val = 1, pc_plot.var = "wiPctChg", raw.plot.var = "percent_change"){
# q.pc <- quantile(scores.df[, pc], probs = q)
# q.ids <- scores.df[scores.df[, pc] > q.pc, id]
#
# q.df <- pc_plot.df[pc_plot.df[, id] %in% q.ids, ]
#
# colors <- c("Pop.Mean" = "black", "+2SD" = "red", "-2SD" = "blue")
#
# print(ggplot(data = pc_plot.df, aes(x=frame, y = mu))+
# geom_line(aes(color = "Pop.Mean"))+
# geom_line(aes(x = frame, y = mu+ pc_plot.df[, 2+pc.val], color = "+2SD"))+
# geom_line(aes(x = frame, y = mu- pc_plot.df[, 2+pc.val], color = "-2SD"))+
# labs(x = "frame",
# y = "Percent Change",
# color = "Legend",
# title = paste0("Individuals in the ", q*100,"th Percentile of PC1 scores"))+
# geom_line(data = q.df, aes(x=frame, y = pc_plot.var, group = id))+
# #scale_color_manual(values = colors)+
# theme_bw())
#
# for(i in q.ids){
# print(ggplot(data = raw.df[raw.df[, id] == i, ],
# aes(x = frame, y = raw.plot.var, group = tp, color = tp))+
# geom_line()+
# labs(title = paste0(i, " Pre/Post: ", q*100 , "th Percentile PC1 scores"))+
# theme_bw())
# }
# }
# pc_ind_plots(scores.df = wi.scores,
# raw.df = right_0.df,
# pc_plot.df = right_0.pt,
# q = 0.95, pc = "PC1", id = "subject_id", pc.val = 1, pc_plot.var = "wiPctChg", raw.plot.var = "percent_change")
q.95 <- quantile(wi.scores$PC1, probs = 0.95)
pctile95.wi <- wi.scores$subject_id[wi.scores$PC1 > q.95]
pctile95.wi.df <- right_0.pt[right_0.pt$subject_id %in% pctile95.wi, ]
ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband1, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband1, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = "Individuals in the Top 5th Percentile of PC1 scores")+
geom_line(data = pctile95.wi.df, aes(x=frame/30, y = wiPctChg, group = subject_id, color = subject_id))+
scale_color_manual(values = colors)+
theme_bw()
for(i in pctile95.wi){
print(ggplot(data = right_0.post[right_0.post$subject_id == i, ],
aes(x = seconds, y = percent_change, group = tp, color = tp))+
geom_line()+
labs(title = paste0(i, " Pre/Post: Top 5th Percentile PC1 scores"))+
theme_bw())
}
q.05 <- quantile(wi.scores$PC1, probs = 0.05)
pctile05.wi <- wi.scores$subject_id[wi.scores$PC1 < q.05]
pctile05.wi.df <- right_0.pt[right_0.pt$subject_id %in% pctile05.wi, ]
ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband1, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband1, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = "Individuals in the 5th Percentile of PC1 scores")+
geom_line(data = pctile05.wi.df, aes(x=frame/30, y = wiPctChg, group = subject_id, color = subject_id))+
scale_color_manual(values = colors)+
theme_bw()
for(i in pctile05.wi){
print(ggplot(data = right_0.post[right_0.post$subject_id == i, ],
aes(x = seconds, y = percent_change, group = tp, color = tp))+
geom_line()+
labs(title = paste0(i, " Pre/Post: 5th Percentile PC1 scores"))+
theme_bw())
}q.95 <- quantile(wi.scores$PC2, probs = 0.95)
pctile95.wi <- wi.scores$subject_id[wi.scores$PC2 > q.95]
pctile95.wi.df <- right_0.pt[right_0.pt$subject_id %in% pctile95.wi, ]
ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband2, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband2, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = "Individuals in the Top 5th Percentile of PC2 scores")+
geom_line(data = pctile95.wi.df, aes(x=frame/30, y = wiPctChg, group = subject_id, color = subject_id))+
scale_color_manual(values = colors)+
theme_bw()
for(i in pctile95.wi){
print(ggplot(data = right_0.post[right_0.post$subject_id == i, ],
aes(x = seconds, y = percent_change, group = tp, color = tp))+
geom_line()+
labs(title = paste0(i, " Pre/Post: Top 5th Percentile PC2 scores"))+
theme_bw())
}
q.05 <- quantile(wi.scores$PC2, probs = 0.05)
pctile05.wi <- wi.scores$subject_id[wi.scores$PC2 < q.05]
pctile05.wi.df <- right_0.pt[right_0.pt$subject_id %in% pctile05.wi, ]
ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband2, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband2, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = "Individuals in the 5th Percentile of PC2 scores")+
geom_line(data = pctile05.wi.df, aes(x=frame/30, y = wiPctChg, group = subject_id, color = subject_id))+
scale_color_manual(values = colors)+
theme_bw()
for(i in pctile05.wi){
print(ggplot(data = right_0.post[right_0.post$subject_id == i, ],
aes(x = seconds, y = percent_change, group = tp, color = tp))+
geom_line()+
labs(title = paste0(i, " Pre/Post: 5th Percentile PC2 scores"))+
theme_bw())
}q.95 <- quantile(wi.scores$PC3, probs = 0.95)
pctile95.wi <- wi.scores$subject_id[wi.scores$PC3 > q.95]
pctile95.wi.df <- right_0.pt[right_0.pt$subject_id %in% pctile95.wi, ]
ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband3, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband3, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = "Individuals in the Top 5th Percentile of PC3 scores")+
geom_line(data = pctile95.wi.df, aes(x=seconds, y = wiPctChg, group = subject_id, color = subject_id))+
scale_color_manual(values = colors)+
theme_bw()
for(i in pctile95.wi){
print(ggplot(data = right_0.post[right_0.post$subject_id == i, ],
aes(x = seconds, y = percent_change, group = tp, color = tp))+
geom_line()+
labs(title = paste0(i, " Pre/Post: Top 5th Percentile PC3 scores"))+
theme_bw())
}
q.05 <- quantile(wi.scores$PC3, probs = 0.05)
pctile05.wi <- wi.scores$subject_id[wi.scores$PC3 < q.05]
pctile05.wi.df <- right_0.pt[right_0.pt$subject_id %in% pctile05.wi, ]
ggplot(data = wi.df_plot, aes(x = seconds, y = mu))+
geom_line(aes(color = "Pop.Mean"))+
geom_line(aes(x = seconds, y = mu+errband3, color = "+2SD"))+
geom_line(aes(x = seconds, y = mu-errband3, color = "-2SD"))+
labs(x = "frame",
y = "Percent Change",
color = "Legend",
title = "Individuals in the 5th Percentile of PC3 scores")+
geom_line(data = pctile05.wi.df, aes(x=frame/30, y = wiPctChg, group = subject_id, color = subject_id))+
scale_color_manual(values = colors)+
theme_bw()
for(i in pctile05.wi){
print(ggplot(data = right_0.post[right_0.post$subject_id == i, ],
aes(x = seconds, y = percent_change, group = tp, color = tp))+
geom_line()+
labs(title = paste0(i, " Pre/Post: 5th Percentile PC3 scores"))+
theme_bw())
}10 Meeting 20220908
### INVESTIGATE BUMPS IN MEAN Function
## Pre/Post Across subjects
ggplot(mean_fxn.l, aes(x=frame/30, y = percent_change, group = grp,
color = user_cat))+
geom_line()+
xlim(50, 200)+
facet_grid(rows = vars(tp))+
labs(title = "Average Percent Change by Pre/Post and THC use category",
x = "seconds")+
theme_bw()
right_0.w[right_0.w$user_cat == "non-user" & right_0.w$tp == "pre2", 103:105]
right_0.w[right_0.w$user_cat == "non-user" & right_0.w$tp == "post", 101:103]
## Within person plots
ggplot(mean_fxn.pt.l, aes(x=frame/30, y = wi_percent_change, group = user_cat, color = user_cat))+
geom_line()+
xlim(50, 200)+
labs(title ="Average Within subject difference in percent change by THC use category",
x = "seconds")+
theme_bw()
## Mean function Spike in Occassional user group
mean_fxn.pt[mean_fxn.pt$Group.1 == "occasional", 113:119]
right_0.pt.w[right_0.pt.w$user_cat == "occasional", 116:119]
## Mean function Spike in Daily user group
mean_fxn.pt[mean_fxn.pt$Group.1 == "daily", 133:149]
right_0.pt.w[right_0.pt.w$user_cat == "daily", 143:146]Jagged changes in the mean function lines seems to stem from missing data at those frames in the data set. The subjects per user-group is between 25 - 30, so missing data for one individual has a noticeable effect on mean function line.
11 Analysis
11.1 POST DATA: Logistic Regression models to predict Smoker vs non-smoker using post data. plot AUC
## png
## 211.2 Compare AUC across models
auc.df$AUC <- round(as.numeric(auc.df$AUC), 3)
knitr::kable(auc.df)| Label | AUC |
|---|---|
| Post AUC PCs | 0.681 |
| Post AUC Scalar Feat | 0.675 |
| W/I Diff AUC PCS | 0.710 |
| W/I Diff AUC Scalar Feat | 0.681 |
AUC Inference
Vr_10 <- function(nN, srpi, srnj){
Vr_10i <- vector(mode = "numeric", length = length(srpi))
for(i in 1:length(srpi)){
Vr_10i[i] = (1/nN)*(sum(srpi[i] > srnj)+ 0.5*sum(srpi[i] == srnj))
}
return(Vr_10i)
}
Vr_01 <- function(pN, srpi, srnj){
Vr_01i <- vector(mode = "numeric", length = length(srnj))
for(i in 1:length(srnj)){
Vr_01i[i] = (1/pN)*(sum(srnj[i] < srpi)+ 0.5*sum(srnj[i] == srpi))
}
return(Vr_01i)
}
w10 <- function(nP, v10_r, auc_r, v10_s, auc_s){
diff_r <- vector(mode = "numeric", length = length(v10_r))
diff_s <- vector(mode = "numeric", length = length(v10_s))
for(i in 1:length(v10_r)){
diff_r[i] <- v10_r[i] - auc_r
diff_s[i] <- v10_s[i] - auc_r
}
res <- (nP-1)^(-1)* sum(diff_r * diff_s)
return(res)
}
w01 <- function(nN, v01_r, auc_r, v01_s, auc_s){
diff_r <- vector(mode = "numeric", length = length(v01_r))
diff_s <- vector(mode = "numeric", length = length(v01_s))
for(i in 1:length(v01_r)){
diff_r[i] <- v01_r[i] - auc_r
diff_s[i] <- v01_s[i] - auc_r
}
res <- (nN-1)^(-1)* sum(diff_r * diff_s)
return(res)
}
# w10_postPC_postSF <- w10(nP = sum(auc_inference$smoker == 1),
# v10_r = V_postPC_10,
# auc_r = auc.df$AUC[auc.df$Label == "Post AUC PCs"],
# v10_s = V_postSF_10,
# auc_s = auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"])
#
# w01_postPC_postSF <- w01(nN = sum(auc_inference$smoker == 0),
# v01_r = V_postPC_01,
# auc_r = auc.df$AUC[auc.df$Label == "Post AUC PCs"],
# v01_s = V_postSF_01,
# auc_s = auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"])
w_rs <- function(nP, nN, srpi_r, srnj_r, srpi_s, srnj_s, auc_r, auc_s){
v10_r <- Vr_10(nN, srpi_r, srnj_r)
v01_r <- Vr_01(pN=nP, srpi_r, srnj_r)
v10_s <- Vr_10(nN, srpi_s, srnj_s)
v01_s <- Vr_01(pN=nP, srpi_s, srnj_s)
w10_rs <- w10(nP, v10_r, auc_r, v10_s, auc_s)
w01_rs <- w01(nN, v01_r, auc_r, v01_s, auc_s)
res <- (nP)^(-1)*w10_rs + (nN)^(-1)*w01_rs
return(res)
}
w_postPC_postPC <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$post_PC_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$post_PC_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$post_PC_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$post_PC_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "Post AUC PCs"],
auc_s = auc.df$AUC[auc.df$Label == "Post AUC PCs"])
w_postSF_postSF <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$post_SF_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$post_SF_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$post_SF_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$post_SF_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"],
auc_s = auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"])
w_wpdPC_wpdPC <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "W/I Diff AUC PCS"],
auc_s = auc.df$AUC[auc.df$Label == "W/I Diff AUC PCS"])
w_wpdSF_wpdSF <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "W/I Diff AUC Scalar Feat"],
auc_s = auc.df$AUC[auc.df$Label == "W/I Diff AUC Scalar Feat"])
w_postPC_postSF <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$post_PC_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$post_PC_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$post_SF_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$post_SF_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "Post AUC PCs"],
auc_s = auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"])
w_postPC_wpdPC <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$post_PC_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$post_PC_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "Post AUC PCs"],
auc_s = auc.df$AUC[auc.df$Label == "W/I Diff AUC PCS"])
w_postPC_wpdSF <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$post_PC_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$post_PC_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "Post AUC PCs"],
auc_s = auc.df$AUC[auc.df$Label == "W/I Diff AUC Scalar Feat"])
w_postSF_wpdPC <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$post_SF_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$post_SF_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"],
auc_s = auc.df$AUC[auc.df$Label == "W/I Diff AUC PCS"])
w_postSF_wpdSF <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$post_SF_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$post_SF_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"],
auc_s = auc.df$AUC[auc.df$Label == "W/I Diff AUC Scalar Feat"])
w_wpdPC_wpdSF <- w_rs(nP = sum(auc_inference$smoker == 1),
nN = sum(auc_inference$smoker == 0),
srpi_r = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 1],
srnj_r = auc_inference$wpd_PC_aucScores[auc_inference$smoker == 0],
srpi_s = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 1],
srnj_s = auc_inference$wpd_SF_aucScores[auc_inference$smoker == 0],
auc_r = auc.df$AUC[auc.df$Label == "W/I Diff AUC PCS"],
auc_s = auc.df$AUC[auc.df$Label == "W/I Diff AUC Scalar Feat"])
z_postPC_postSF <- (auc.df$AUC[auc.df$Label == "Post AUC PCs"] - auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"])/
(sqrt(w_postPC_postPC + w_postSF_postSF - w_postPC_postSF))
z_postPC_wpdPC <- (auc.df$AUC[auc.df$Label == "Post AUC PCs"] - auc.df$AUC[auc.df$Label == "W/I Diff AUC PCS"])/
(sqrt(w_postPC_postPC + w_wpdPC_wpdPC - w_postPC_wpdPC))
z_postPC_wpdSF <- (auc.df$AUC[auc.df$Label == "Post AUC PCs"] - auc.df$AUC[auc.df$Label == "W/I Diff AUC Scalar Feat"])/
(sqrt(w_postPC_postPC + w_wpdSF_wpdSF - w_postPC_wpdSF))
z_postSF_wpdPC <- (auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"] - auc.df$AUC[auc.df$Label == "W/I Diff AUC PCS"])/
(sqrt(w_postSF_postSF + w_wpdPC_wpdPC - w_postSF_wpdPC))
z_postSF_wpdSF <- (auc.df$AUC[auc.df$Label == "Post AUC Scalar Feat"] - auc.df$AUC[auc.df$Label == "W/I Diff AUC Scalar Feat"])/
(sqrt(w_postSF_postSF + w_wpdSF_wpdSF - w_postSF_wpdSF))
z_wpdPC_wpdSF <- (auc.df$AUC[auc.df$Label == "W/I Diff AUC PCS"] - auc.df$AUC[auc.df$Label == "W/I Diff AUC Scalar Feat"])/
(sqrt(w_wpdSF_wpdSF + w_wpdPC_wpdPC - w_wpdPC_wpdSF))
auc_compare <- data.frame("Test Desciption" = c("AUC-postPC v AUC-postSF",
"AUC-postPC v AUC-wpdPC",
"AUC-postPC v AUC-wpdSF",
"AUC-postSF v AUC-wpdPC",
"AUC-postSF v AUC-wpdPC",
"AUC-wpdPC v AUC-wpdSF"),
"z" = c(round(z_postPC_postSF, 3),
round(z_postPC_wpdPC, 3),
round(z_postPC_wpdSF, 3),
round(z_postSF_wpdPC, 3),
round(z_postSF_wpdSF, 3),
round(z_wpdPC_wpdSF, 3)))
auc_compare$p <- round((1-pnorm(abs(auc_compare$z)))*2, 3)
knitr::kable(auc_compare)| Test.Desciption | z | p |
|---|---|---|
| AUC-postPC v AUC-postSF | 0.080 | 0.936 |
| AUC-postPC v AUC-wpdPC | -0.351 | 0.726 |
| AUC-postPC v AUC-wpdSF | 0.000 | 1.000 |
| AUC-postSF v AUC-wpdPC | -0.440 | 0.660 |
| AUC-postSF v AUC-wpdPC | -0.074 | 0.941 |
| AUC-wpdPC v AUC-wpdSF | 0.415 | 0.678 |
11.3 Association with Demog Variables
pt.scores$male <- ifelse(pt.scores$demo_gender == "Male", 1, 0)
pt.scores$non_user <- ifelse(pt.scores$user_cat == "non-user", 1, 0)
pt.scores$daily <- ifelse(pt.scores$user_cat == "daily", 1, 0)
pt.scores$occasional <- ifelse(pt.scores$user_cat == "occasional", 1, 0)
pt.wi.scores$male <- ifelse(pt.wi.scores$demo_gender == "Male", 1, 0)
pt.wi.scores$non_user <- ifelse(pt.wi.scores$user_cat == "non-user", 1, 0)
pt.wi.scores$daily <- ifelse(pt.wi.scores$user_cat == "daily", 1, 0)
pt.wi.scores$occasional <- ifelse(pt.wi.scores$user_cat == "occasional", 1, 0)11.3.1 Post Data
Compare PCs to Scalar Features
chart.Correlation(pt.scores[, c("PC1", "PC2", "PC3", "PC4",
"min_constriction.post", "duration.post",
"avg_end_percent.post", "slope.post", "AUC.post")])
Compare PCs to demog Variables
chart.Correlation(pt.scores[, c("PC1", "PC2", "PC3", "PC4",
"demo_age","male", "thc.post", "thc_chg", "BMI",
"postConsumpTimeToTest", "smoker",
"non_user", "daily", "occasional")])## Warning in cor(x, y, use = use, method = method): the standard deviation is zero## Warning in cor(x, y): the standard deviation is zero## Warning in cor(x, y, use = use, method = method): the standard deviation is zero## Warning in cor(x, y): the standard deviation is zero
Compare Scalar Features to demog variables
chart.Correlation(pt.scores[, c("min_constriction.post", "duration.post",
"avg_end_percent.post", "slope.post", "AUC.post",
"demo_age","male", "thc.post", "thc_chg", "BMI",
"postConsumpTimeToTest", "smoker",
"non_user", "daily", "occasional")])## Warning in cor(x, y, use = use, method = method): the standard deviation is zero## Warning in cor(x, y): the standard deviation is zero## Warning in cor(x, y, use = use, method = method): the standard deviation is zero## Warning in cor(x, y): the standard deviation is zero
post.age.m <- lm(demo_age ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
summary(post.age.m)##
## Call:
## lm(formula = demo_age ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.6845 -4.3705 -0.2025 3.0915 12.7315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.011107 0.540577 59.217 <2e-16 ***
## PC1 -0.004604 0.003968 -1.161 0.2493
## PC2 0.021247 0.011155 1.905 0.0605 .
## PC3 -0.016639 0.017201 -0.967 0.3363
## PC4 -0.014717 0.025677 -0.573 0.5682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.954 on 79 degrees of freedom
## Multiple R-squared: 0.07418, Adjusted R-squared: 0.0273
## F-statistic: 1.582 on 4 and 79 DF, p-value: 0.1872post.wt.m <- lm(demo_weight ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
summary(post.wt.m)##
## Call:
## lm(formula = demo_weight ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -73.326 -29.368 -5.404 21.755 104.387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 171.66399 4.21253 40.751 <2e-16 ***
## PC1 -0.01098 0.03092 -0.355 0.723
## PC2 0.11151 0.08692 1.283 0.203
## PC3 -0.11148 0.13404 -0.832 0.408
## PC4 -0.26579 0.20009 -1.328 0.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38.61 on 79 degrees of freedom
## Multiple R-squared: 0.05157, Adjusted R-squared: 0.003548
## F-statistic: 1.074 on 4 and 79 DF, p-value: 0.3751post.ht.m <- lm(demo_height ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
summary(post.ht.m)##
## Call:
## lm(formula = demo_height ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.6843 -2.8328 -0.1437 2.5709 9.4686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 68.695465 0.459844 149.389 <2e-16 ***
## PC1 -0.002579 0.003375 -0.764 0.447
## PC2 -0.007534 0.009489 -0.794 0.430
## PC3 -0.002316 0.014632 -0.158 0.875
## PC4 -0.015311 0.021842 -0.701 0.485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.214 on 79 degrees of freedom
## Multiple R-squared: 0.02152, Adjusted R-squared: -0.02802
## F-statistic: 0.4344 on 4 and 79 DF, p-value: 0.7834post.bmi.m <- lm(BMI ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
summary(post.bmi.m)##
## Call:
## lm(formula = BMI ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.507 -3.132 -0.392 2.565 8.977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.4085955 0.4696584 54.100 <2e-16 ***
## PC1 -0.0002697 0.0034470 -0.078 0.9378
## PC2 0.0214059 0.0096912 2.209 0.0301 *
## PC3 -0.0104595 0.0149447 -0.700 0.4861
## PC4 -0.0366002 0.0223084 -1.641 0.1048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.304 on 79 degrees of freedom
## Multiple R-squared: 0.09332, Adjusted R-squared: 0.04741
## F-statistic: 2.033 on 4 and 79 DF, p-value: 0.09779post.thc.m <- lm(thc_chg ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
summary(post.thc.m)##
## Call:
## lm(formula = thc_chg ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.731 -11.175 -8.500 -0.014 111.024
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.89476 2.45021 4.855 6.08e-06 ***
## PC1 0.01352 0.01788 0.756 0.452
## PC2 -0.01683 0.05026 -0.335 0.739
## PC3 0.06726 0.07774 0.865 0.390
## PC4 -0.05451 0.11573 -0.471 0.639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.32 on 78 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02048, Adjusted R-squared: -0.02975
## F-statistic: 0.4077 on 4 and 78 DF, p-value: 0.8026post.consump.m <- lm(postConsumpTimeToTest ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
summary(post.consump.m)##
## Call:
## lm(formula = postConsumpTimeToTest ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.3643 -3.2540 -0.4397 2.7332 19.2217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.615735 0.792896 78.971 <2e-16 ***
## PC1 -0.007264 0.006659 -1.091 0.281
## PC2 0.029166 0.018235 1.599 0.116
## PC3 -0.022868 0.031685 -0.722 0.474
## PC4 0.025181 0.039465 0.638 0.526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.544 on 50 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.08165, Adjusted R-squared: 0.008178
## F-statistic: 1.111 on 4 and 50 DF, p-value: 0.3616post.male.m <- glm(male ~ PC1 + PC2 + PC3 + PC4, family = "binomial", data = pt.scores)
summary(post.male.m)##
## Call:
## glm(formula = male ~ PC1 + PC2 + PC3 + PC4, family = "binomial",
## data = pt.scores)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1788 -1.1988 0.7545 1.0495 1.3019
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.378168 0.231748 1.632 0.103
## PC1 -0.002916 0.001838 -1.586 0.113
## PC2 -0.005282 0.005524 -0.956 0.339
## PC3 -0.003360 0.007911 -0.425 0.671
## PC4 -0.015126 0.011813 -1.280 0.200
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 114.10 on 83 degrees of freedom
## Residual deviance: 108.65 on 79 degrees of freedom
## AIC: 118.65
##
## Number of Fisher Scoring iterations: 411.3.2 Within Person Difference Data
Compare PCs to Scalar Features
chart.Correlation(pt.wi.scores[, c("PC1", "PC2", "PC3", "PC4", "PC5", "PC6",
"min_constriction_chg", "AUC_chg", "duration_chg",
"avg_end_percent_chg", "slope_chg")])
Compare PCs to demog Variables
chart.Correlation(pt.wi.scores[, c("PC1", "PC2", "PC3", "PC4", "PC5", "PC6",
"demo_age","male", "thc.post", "thc_chg", "BMI",
"postConsumpTimeToTest", "smoker",
"non_user", "daily", "occasional")])
Compare Scalar Features to demog variables
chart.Correlation(pt.wi.scores[, c("min_constriction_chg", "AUC_chg", "duration_chg",
"avg_end_percent_chg", "slope_chg",
"demo_age","male", "thc.post", "thc_chg", "BMI",
"postConsumpTimeToTest", "smoker",
"non_user", "daily", "occasional")])## Warning in cor(x, y, use = use, method = method): the standard deviation is zero## Warning in cor(x, y): the standard deviation is zero## Warning in cor(x, y, use = use, method = method): the standard deviation is zero## Warning in cor(x, y): the standard deviation is zero
wi.age.m <- lm(demo_age ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
summary(wi.age.m)##
## Call:
## lm(formula = demo_age ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.9988 -3.4891 -0.7743 2.7927 13.5604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.002624 0.548036 58.395 <2e-16 ***
## PC1 0.001966 0.003700 0.531 0.597
## PC2 -0.009954 0.007303 -1.363 0.177
## PC3 -0.007683 0.010012 -0.767 0.445
## PC4 0.015526 0.013419 1.157 0.251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.022 on 79 degrees of freedom
## Multiple R-squared: 0.0488, Adjusted R-squared: 0.0006381
## F-statistic: 1.013 on 4 and 79 DF, p-value: 0.4057wi.wt.m <- lm(demo_weight ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
summary(wi.wt.m)##
## Call:
## lm(formula = demo_weight ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -89.567 -25.100 -3.635 19.597 98.808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 171.56514 4.10429 41.801 <2e-16 ***
## PC1 0.05566 0.02771 2.009 0.048 *
## PC2 -0.06805 0.05469 -1.244 0.217
## PC3 -0.08688 0.07498 -1.159 0.250
## PC4 0.13802 0.10049 1.373 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37.61 on 79 degrees of freedom
## Multiple R-squared: 0.1, Adjusted R-squared: 0.05444
## F-statistic: 2.195 on 4 and 79 DF, p-value: 0.07709wi.ht.m <- lm(demo_height ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
summary(wi.ht.m)##
## Call:
## lm(formula = demo_height ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.0426 -3.2507 0.1108 2.2040 8.9792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 68.677130 0.454452 151.121 <2e-16 ***
## PC1 0.002125 0.003068 0.693 0.491
## PC2 -0.006224 0.006056 -1.028 0.307
## PC3 -0.004791 0.008302 -0.577 0.566
## PC4 0.015086 0.011127 1.356 0.179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.164 on 79 degrees of freedom
## Multiple R-squared: 0.04468, Adjusted R-squared: -0.003688
## F-statistic: 0.9238 on 4 and 79 DF, p-value: 0.4545wi.bmi.m <- lm(BMI ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
summary(wi.bmi.m)##
## Call:
## lm(formula = BMI ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.7935 -2.7105 -0.9652 2.5352 8.8133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.406031 0.473423 53.665 <2e-16 ***
## PC1 0.006406 0.003196 2.004 0.0484 *
## PC2 -0.005772 0.006309 -0.915 0.3630
## PC3 -0.009432 0.008649 -1.091 0.2788
## PC4 0.010097 0.011592 0.871 0.3864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.338 on 79 degrees of freedom
## Multiple R-squared: 0.07906, Adjusted R-squared: 0.03243
## F-statistic: 1.696 on 4 and 79 DF, p-value: 0.1593wi.thc.m <- lm(thc_chg ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
summary(wi.thc.m)##
## Call:
## lm(formula = thc_chg ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.899 -11.063 -6.854 -0.935 109.588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.824855 2.423977 4.878 5.54e-06 ***
## PC1 0.023979 0.016379 1.464 0.147
## PC2 0.013853 0.032118 0.431 0.667
## PC3 0.045109 0.044151 1.022 0.310
## PC4 -0.006963 0.059020 -0.118 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.08 on 78 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.04183, Adjusted R-squared: -0.007308
## F-statistic: 0.8513 on 4 and 78 DF, p-value: 0.4971wi.consump.m <- lm(postConsumpTimeToTest ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
summary(wi.consump.m)##
## Call:
## lm(formula = postConsumpTimeToTest ~ PC1 + PC2 + PC3 + PC4, data = pt.wi.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.6270 -3.2816 -0.2987 2.3024 20.4466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.778834 0.783715 78.828 <2e-16 ***
## PC1 0.003755 0.005617 0.668 0.507
## PC2 0.011788 0.011540 1.022 0.312
## PC3 0.024460 0.014610 1.674 0.100
## PC4 -0.005263 0.017605 -0.299 0.766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.554 on 50 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.07836, Adjusted R-squared: 0.004631
## F-statistic: 1.063 on 4 and 50 DF, p-value: 0.3847wi.male.m <- glm(male ~ PC1 + PC2 + PC3 + PC4, family = "binomial", data = pt.wi.scores)
summary(wi.male.m)##
## Call:
## glm(formula = male ~ PC1 + PC2 + PC3 + PC4, family = "binomial",
## data = pt.wi.scores)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6262 -1.1842 0.6667 1.0082 1.6770
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3857856 0.2349240 1.642 0.1006
## PC1 0.0025223 0.0016333 1.544 0.1225
## PC2 -0.0068811 0.0035516 -1.937 0.0527 .
## PC3 -0.0063244 0.0047521 -1.331 0.1832
## PC4 -0.0002728 0.0059838 -0.046 0.9636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 114.10 on 83 degrees of freedom
## Residual deviance: 106.34 on 79 degrees of freedom
## AIC: 116.34
##
## Number of Fisher Scoring iterations: 4Demographic Variables:
- Age
- Weight
- Height
- BMI
- Change in THC
- Time from consumption to Post test
- Sex
Results:
Regression models regressed demographic variables on the 4 PCs for the post data.
- BMI negatively associated with PC2 adjusting for other PCs (p= 0.03)
Regression models regressed demographic variables on the 6 PCs for the WPD data.
- Weight negatively associated with PC2 & positively associated with PC5 adjusting for other PCs (p = 0.04 & 0.009)
- Height positively associated with PC5 adjusting for other PCs (p = 0.006)
- BMI positively associated with PC1 adjusting for other PCs (p= 0.049)
- Male positively associated with PC5 adjusting for other PCs (p= 0.040)
11.4 Backward stepwise selection
11.5 Function on Scalar Regression
11.5.1 Post
\[ \begin{aligned} Y_{i}(t) &= f_0(t) + f_1(t)*I(user = occasional) + f_2(t)*I(user = daily) + b_i(t) + \epsilon_i(t)\\ & b_i(t) \sim GP(0, \Sigma_b) \\ & \epsilon_i(t) \sim N(0, \sigma_{\epsilon}^2) \\ &= \sum_{k=1}^K \xi_{0k}\phi_{0k}(t_i) + \sum_{k=1}^K \xi_{1k}\phi_{1k}(t_i)*I(user = occasional) + \sum_{k=1}^K \xi_{2k}\phi_{2k}(t_i)*I(user = daily) + \sum_{k=1}^K \xi_{ik}\phi_k(t_i) + \epsilon_i(t)\\ \end{aligned} \]
11.5.2 Mean function for non-user
\[ \begin{aligned} Y_{i}(t) &= f_0(t)\\ &=\sum_{k=1}^K \xi_{0k}\phi_{0k}(t_i) \\ &= \Phi_0\xi_0 \end{aligned} \]
11.5.3 Mean function for occasional user
\[ \begin{aligned} Y_{i}(t) &= f_0(t) + f_1(t) \\ &=\sum_{k=1}^K \xi_{0k}\phi_{0k}(t_i) + \sum_{k=1}^K \xi_{1k}\phi_{1k}(t_i)\\ &= \Phi_0\xi_0 + \Phi_1\xi_1\\ &= [ \Phi_0, \Phi_1 ] \xi \end{aligned} \]
11.5.4 Mean function for daily user
\[ \begin{aligned} Y_{i}(t) &= f_0(t) + f_2(t) \\ &=\sum_{k=1}^K \xi_{0k}\phi_{0k}(t_i) + \sum_{k=1}^K \xi_{2k}\phi_{2k}(t_i) \\ &= \Phi_0\xi_0 + \Phi_2\xi_2\\ &= [ \Phi_0, \Phi_2 ] \xi \end{aligned} \]
pt.analytic.df <- pt.df[pt.df$subject_id %in% analytic.N, ]
pt.analytic.df$occasional <- ifelse(pt.analytic.df$user_cat == "occasional", 1, 0)
pt.analytic.df$daily <- ifelse(pt.analytic.df$user_cat == "daily", 1, 0)
pt.analytic.df <- pt.analytic.df[order(pt.analytic.df$subject_id), ]
right_0.post.w <- right_0.post.w[order(right_0.post.w$subject_id), ]
post_pffr.df <- data.frame("subject_id" = as.factor(pt.analytic.df$subject_id),
"occ_user" = pt.analytic.df$occasional,
"daily_user" = pt.analytic.df$daily,
"pct_chg" = I(as.matrix(right_0.post.w[, c(4:404)])))
m.post_pffr <- pffr(pct_chg ~ occ_user + daily_user +s(subject_id, bs="re"),
data = post_pffr.df,
algorithm = "bam",
method = "fREML",
discrete = T)
summary(m.post_pffr)
par(mfrow = c(1, 3))
plot(m.post_pffr)What does NA in the output mean? Did not find NA in either the matrix or other variables
# head(right_0.df)
right_0.gam.df <- merge(right_0.post, pt.analytic.df,
by = c("subject_id", "user_cat"))
# right_0.gam.df <- right_0.gam.df[order(right_0.gam.df$subject_id, right_0.gam.df$frame), ]
right_0.gam.df$sind <- right_0.gam.df$frame/400
# head(right_0.gam.df)
m.post_gam <- mgcv::gam(percent_change ~ s(sind, k=30, bs="cr") +
s(sind, by=occasional, k=30, bs = "cr") +
s(sind, by=daily, k=30, bs = "cr"),
data = right_0.gam.df, method = "REML")
## Create a matrix of residuals
post_gam.resid <- cbind(right_0.gam.df[!(is.na(right_0.gam.df$percent_change)),
c("subject_id", "frame")],
m.post_gam$residuals)
names(post_gam.resid) <- c("subject_id", "frame", "resid")
# post_gam.resid$frame_char <- str_pad(post_gam.resid$frame, width = 3, side = "left", pad = "0")
resid_mat <- reshape(post_gam.resid[, c("subject_id", "frame", "resid")],
timevar = "frame",
idvar = "subject_id",
direction = "wide")
rownames(resid_mat) <- resid_mat$subject_id
resid_mat$subject_id <- NULL
# resid_names <- names(resid_mat)
# resid_names <- sort(resid_names)
# resid_mat <- resid_mat[, resid_names]
resid_mat <- as.matrix(resid_mat)
post_gam.fpca <- fpca.face(resid_mat, knots = 15)
#Create data frame of eigen functions and attach to original data
Phi_mat <- as.data.frame(post_gam.fpca$efunctions)
colnames(Phi_mat) <- paste0("Phi", seq(1, post_gam.fpca$npc))
Phi_mat$frame <- as.numeric(rownames(Phi_mat)) -1
right_0.gam.df <- merge(right_0.gam.df, Phi_mat,
by = "frame",
all.x = T)
right_0.gam.df <- right_0.gam.df[order(right_0.gam.df$subject_id, right_0.gam.df$frame), ]
right_0.gam.df$subject_id <- as.factor(right_0.gam.df$subject_id)
post_gam.fri <- mgcv::bam(percent_change ~
s(sind, k=30, bs="cr") +
s(sind, by=occasional, k=30, bs = "cr") +
s(sind, by=daily, k=30, bs = "cr") +
s(subject_id, by = Phi1, bs="re") + s(subject_id, by = Phi2, bs="re")+
s(subject_id, by = Phi3, bs="re") + s(subject_id, by = Phi4, bs="re"),
method = "fREML", data = right_0.gam.df, discrete = TRUE)
summary(post_gam.fri)##
## Family: gaussian
## Link function: identity
##
## Formula:
## percent_change ~ s(sind, k = 30, bs = "cr") + s(sind, by = occasional,
## k = 30, bs = "cr") + s(sind, by = daily, k = 30, bs = "cr") +
## s(subject_id, by = Phi1, bs = "re") + s(subject_id, by = Phi2,
## bs = "re") + s(subject_id, by = Phi3, bs = "re") + s(subject_id,
## by = Phi4, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.3125 0.9197 -11.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(sind) 27.54 28.43 115.67 <2e-16 ***
## s(sind):occasional 18.63 21.95 70.14 <2e-16 ***
## s(sind):daily 18.62 21.95 35.42 <2e-16 ***
## s(subject_id):Phi1 82.23 84.00 23472.92 <2e-16 ***
## s(subject_id):Phi2 81.68 84.00 2775.02 <2e-16 ***
## s(subject_id):Phi3 82.23 84.00 887.87 <2e-16 ***
## s(subject_id):Phi4 81.05 84.00 1139.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.966 Deviance explained = 96.6%
## fREML = 63334 Scale est. = 2.6314 n = 3264211.5.5 Plotting trajectory of occasional & daily user
post_gam.coef <- post_gam.fri$coefficients
post_gam.cov <- vcov.gam(post_gam.fri)
df_pred_non <- data.frame("sind" = unique(right_0.gam.df$sind), "occasional" = 0, "daily" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = right_0.gam.df$subject_id[1])
lpmat_non <- predict(post_gam.fri, newdata=df_pred_non, se.fit=TRUE, type = "lpmatrix")
df_pred_occ <- data.frame("sind" = unique(right_0.gam.df$sind), "occasional" = 1, "daily" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = right_0.gam.df$subject_id[1])
lpmat_occ <- predict(post_gam.fri, newdata=df_pred_occ, se.fit=TRUE, type = "lpmatrix")
df_pred_dly <- data.frame("sind" = unique(right_0.gam.df$sind), "occasional" = 0, "daily" = 1,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = right_0.gam.df$subject_id[1])
lpmat_dly <- predict(post_gam.fri, newdata=df_pred_dly, se.fit=TRUE, type = "lpmatrix")
pred_df <- data.frame(seconds = rep(seq(0, 400), 3)/30,
user = c(rep("non-user", 401), rep("occasional", 401),rep("daily", 401)),
mean = c(lpmat_non %*% post_gam.coef,
lpmat_occ %*% post_gam.coef, lpmat_dly %*% post_gam.coef),
se = c(sqrt(diag(lpmat_non %*% post_gam.cov %*% t(lpmat_non))),
sqrt(diag(lpmat_occ %*% post_gam.cov %*% t(lpmat_occ))),
sqrt(diag(lpmat_dly %*% post_gam.cov %*% t(lpmat_dly)))),
stringsAsFactors = F)
post_userProfile <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
labs(y = "Percent Change")+
theme_bw()
legend_userProfile <- g_legend(post_userProfile)
post_userProfile.se <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
geom_line(aes(x=seconds, y = mean + 2*se, group = user, col = user),
linetype = "longdash")+
geom_line(aes(x=seconds, y = mean - 2*se, group = user, col = user),
linetype = "longdash")+
labs(y = "")+
theme_bw()
post_userTraj <- grid.arrange(arrangeGrob(post_userProfile+theme(legend.position = "none"),
post_userProfile.se+theme(legend.position = "none"), nrow = 1),
legend_userProfile, nrow = 1,
widths = c(30, 6))
pred_f1 <- predict(post_gam.fri, newdata=df_pred_occ, se.fit=TRUE, type = "terms")
pred_f2 <- predict(post_gam.fri, newdata=df_pred_dly, se.fit=TRUE, type = "terms")
pred_coef_df <- data.frame(seconds = seq(0, 400)/30,
f0_hat = lpmat_non %*% post_gam.coef,
f0_se = sqrt(diag(lpmat_non %*% post_gam.cov %*% t(lpmat_non))),
f1_hat = pred_f1$fit[, 2],
f1_se = pred_f1$se.fit[, 2],
f2_hat = pred_f2$fit[, 3],
f2_se = pred_f2$se.fit[, 3])
post_non_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f0_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f0_hat + 2*f0_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f0_hat - 2*f0_se), linetype = "longdash")+
labs(title = "Average Percent Change of a Non-user (Post)", ylab = "Percent Change")+
theme_bw()
post_occ_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f1_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f1_hat + 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f1_hat - 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Percent Change: Occasional and Non-user (Post)",
y= "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
post_dly_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f2_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f2_hat + 2*f2_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f2_hat - 2*f2_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Percent Change: Daily and Non-user (Post)",
y = "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
ylab <- textGrob(label = "Difference in Percent Change", rot = 90,
gp = gpar(fontsize = 12))
posval <- textGrob(label = sapply(strwrap("Less Constriction in User", width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
negval <- textGrob(label = sapply(strwrap("More Constriction in User", width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
post_occ_coef <- grid.arrange(ylab, posval, negval, post_occ_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))
post_dly_coef <- grid.arrange(ylab, posval, negval, post_dly_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))
Plot difference between daily and occasional user
f2_f1 <- (lpmat_dly - lpmat_occ) %*% post_gam.coef
f2_f1.se <- sqrt(diag((lpmat_dly - lpmat_occ) %*% post_gam.cov %*% t((lpmat_dly - lpmat_occ))))
f2_f1_diff_df <- data.frame(seconds = seq(0, 400)/30,
f2f1_diff = f2_f1,
f2f1_se = f2_f1.se)
ggplot(data = f2_f1_diff_df, aes(x=seconds, y = f2f1_diff))+
geom_line()+
geom_line(aes(x = seconds, y = f2f1_diff + 2*f2f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f2f1_diff - 2*f2f1_se), linetype = "longdash")+
labs(title = "Difference between Average Daily vs Occasional User", ylab = "f2_hat - f1_hat")+
theme_bw()
post_dlyocc_plt <- ggplot(data = f2_f1_diff_df, aes(x=seconds, y = f2f1_diff))+
geom_line()+
geom_line(aes(x = seconds, y = f2f1_diff + 2*f2f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f2f1_diff - 2*f2f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Percent Change: Daily vs Occasional User",
y = "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
ylab <- textGrob(label = "Difference in Percent Change", rot = 90,
gp = gpar(fontsize = 12))
posval <- textGrob(label = sapply(strwrap("Less Constriction in Daily User", width = 15,
simplify = F),
paste, collapse = "\n"), rot = 0, just = "bottom",
gp = gpar(fontsize = 8))
negval <- textGrob(label = sapply(strwrap("More Constriction in Daily User", width = 15,
simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
post_dlyocc_coef <- grid.arrange(ylab, posval, negval, post_dlyocc_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)),
c(1, 3, rep(4, 15))))
post_dlyocc_coef## TableGrob (2 x 17) "arrange": 4 grobs
## z cells name grob
## 1 1 ( 1- 2, 1- 1) arrange text[GRID.text.2941]
## 2 2 ( 1- 1, 2- 2) arrange text[GRID.text.2942]
## 3 3 ( 2- 2, 2- 2) arrange text[GRID.text.2943]
## 4 4 ( 1- 2, 3-17) arrange gtable[layout]11.5.6 Check for differences between smoker vs non-smoker
smoker.gam.df <- merge(right_0.post, pt.analytic.df,
by = c("subject_id", "user_cat"))
smoker.gam.df$sind <- smoker.gam.df$frame/400
smoker.gam.df$smoker <- ifelse(smoker.gam.df$user_cat == "non-user", 0, 1)
m.post_smoker_gam <- mgcv::gam(percent_change ~ s(sind, k=30, bs="cr") +
s(sind, by=smoker, k=30, bs = "cr"),
data = smoker.gam.df, method = "REML")
## Create a matrix of residuals
post_smoker_gam.resid <- cbind(smoker.gam.df[!(is.na(smoker.gam.df$percent_change)),
c("subject_id", "frame")],
m.post_smoker_gam$residuals)
names(post_smoker_gam.resid) <- c("subject_id", "frame", "resid")
# post_smoker_gam.resid$frame_char <- str_pad(post_smoker_gam.resid$frame,
# width = 3, side = "left", pad = "0")
resid_mat <- reshape(post_smoker_gam.resid[, c("subject_id", "frame", "resid")],
timevar = "frame",
idvar = "subject_id",
direction = "wide")
rownames(resid_mat) <- resid_mat$subject_id
resid_mat$subject_id <- NULL
# resid_names <- names(resid_mat)
# resid_names <- sort(resid_names)
# resid_mat <- resid_mat[, resid_names]
resid_mat <- as.matrix(resid_mat)
post_smoker_gam.fpca <- fpca.face(resid_mat, knots = 15)
#Create data frame of eigen functions and attach to original data
Phi_mat <- as.data.frame(post_smoker_gam.fpca$efunctions)
colnames(Phi_mat) <- paste0("Phi", seq(1, post_smoker_gam.fpca$npc))
Phi_mat$frame <- as.numeric(rownames(Phi_mat)) -1
smoker.gam.df <- merge(smoker.gam.df, Phi_mat,
by = "frame",
all.x = T)
smoker.gam.df <- smoker.gam.df[order(smoker.gam.df$subject_id, smoker.gam.df$frame), ]
smoker.gam.df$subject_id <- as.factor(smoker.gam.df$subject_id)
post_smoker_gam.fri <- mgcv::bam(percent_change ~
s(sind, k=30, bs="cr") +
s(sind, by=smoker, k=30, bs = "cr") +
s(subject_id, by = Phi1, bs="re") + s(subject_id, by = Phi2, bs="re")+
s(subject_id, by = Phi3, bs="re") + s(subject_id, by = Phi4, bs="re"),
method = "fREML", data = smoker.gam.df, discrete = TRUE)
summary(post_smoker_gam.fri)##
## Family: gaussian
## Link function: identity
##
## Formula:
## percent_change ~ s(sind, k = 30, bs = "cr") + s(sind, by = smoker,
## k = 30, bs = "cr") + s(subject_id, by = Phi1, bs = "re") +
## s(subject_id, by = Phi2, bs = "re") + s(subject_id, by = Phi3,
## bs = "re") + s(subject_id, by = Phi4, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.3286 0.9645 -10.71 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(sind) 27.31 28.29 114.24 <2e-16 ***
## s(sind):smoker 19.49 22.78 66.85 <2e-16 ***
## s(subject_id):Phi1 82.65 84.00 24462.37 <2e-16 ***
## s(subject_id):Phi2 82.26 84.00 2954.85 <2e-16 ***
## s(subject_id):Phi3 82.56 84.00 943.48 <2e-16 ***
## s(subject_id):Phi4 81.70 84.00 1213.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.965 Deviance explained = 96.6%
## fREML = 63449 Scale est. = 2.6539 n = 32642Plot difference between smoker and non-smoker
post_smoker_gam.coef <- post_smoker_gam.fri$coefficients
post_smoker_gam.cov <- vcov.gam(post_smoker_gam.fri)
df_pred_non <- data.frame("sind" = unique(smoker.gam.df$sind), "smoker" =0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = smoker.gam.df$subject_id[1])
lpmat_non <- predict(post_smoker_gam.fri, newdata=df_pred_non, se.fit=TRUE, type = "lpmatrix")
df_pred_smk <- data.frame("sind" = unique(smoker.gam.df$sind), "smoker" = 1,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = smoker.gam.df$subject_id[1])
lpmat_smk <- predict(post_smoker_gam.fri, newdata=df_pred_smk, se.fit=TRUE, type = "lpmatrix")
pred_df <- data.frame(seconds = rep(seq(0, 400), 2)/30,
user = c(rep("non-user", 401), rep("smoker", 401)),
mean = c(lpmat_non %*% post_smoker_gam.coef,
lpmat_smk %*% post_smoker_gam.coef),
se = c(sqrt(diag(lpmat_non %*% post_smoker_gam.cov %*% t(lpmat_non))),
sqrt(diag(lpmat_smk %*% post_smoker_gam.cov %*% t(lpmat_smk)))),
stringsAsFactors = F)
post_smkProfile <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
scale_y_continuous(limits = c(min(pred_df$mean - 2*pred_df$se),
max(pred_df$mean+2*pred_df$se)))+
labs(y = "Percent Change")+
theme_bw()
legend_smkProfile <- g_legend(post_smkProfile)
post_smkProfile.se <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
geom_line(aes(x=seconds, y = mean + 2*se, group = user, col = user),
linetype = "longdash")+
geom_line(aes(x=seconds, y = mean - 2*se, group = user, col = user),
linetype = "longdash")+
scale_y_continuous(limits = c(min(pred_df$mean - 2*pred_df$se),
max(pred_df$mean+2*pred_df$se)))+
labs(y = "")+
theme_bw()
post_smk <- grid.arrange(arrangeGrob(post_smkProfile+theme(legend.position = "none"),
post_smkProfile.se+theme(legend.position = "none"), nrow = 1),
legend_smkProfile, nrow = 1,
widths = c(30, 6))
# pred_f0 <- predict(post_smoker_gam.fri, newdata=df_pred_non, se.fit=TRUE, type = "iterms")
pred_f1 <- predict(post_smoker_gam.fri, newdata=df_pred_smk, se.fit=TRUE, type = "terms")
pred_coef_df <- data.frame(seconds = seq(0, 400)/30,
f0_hat = lpmat_non %*% post_smoker_gam.coef,
f0_se = sqrt(diag(lpmat_non %*% post_smoker_gam.cov %*% t(lpmat_non))),
f1_hat = pred_f1$fit[, 2],
f1_se = pred_f1$se.fit[, 2])
post_smk_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f1_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f1_hat + 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f1_hat - 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Percent Change: Smoker and Non-smoker",
y = "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
ylab <- textGrob(label = "Difference in Percent Change", rot = 90,
gp = gpar(fontsize = 12))
posval <- textGrob(label = sapply(strwrap("Less Constriction in Smokers",
width = 15, simplify = F),
paste, collapse = "\n"), rot = 0, just = "bottom",
gp = gpar(fontsize = 8))
negval <- textGrob(label = sapply(strwrap("More Constriction in Smokers",
width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
post_smk_coef <- grid.arrange(ylab, posval, negval, post_smk_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))
11.5.7 Within Subject
right_0.pt.w <- right_0.pt.w[order(right_0.pt.w$subject_id), ]
wi_pffr.df <- data.frame("subject_id" = pt.analytic.df$subject_id,
"occ_user" = pt.analytic.df$occasional,
"daily_user" = pt.analytic.df$daily,
"wi_pct_chg" = I(as.matrix(right_0.pt.w[, c(3:403)])))
wi_pffr.df$subject_id <- as.factor(wi_pffr.df$subject_id)
m.wi_pffr <- pffr(wi_pct_chg ~ occ_user + daily_user + s(subject_id, bs="re"),
data = wi_pffr.df,
algorithm = "bam",
method = "fREML",
discrete = T)
summary(m.wi_pffr)
par(mfrow = c(1,3))
plot(m.wi_pffr)right_0.pt.gam.df <- merge(right_0.pt, pt.analytic.df,
by = c("subject_id", "user_cat"))
right_0.pt.gam.df$sind <- right_0.pt.gam.df$frame/400
m.wi_gam <- mgcv::gam(wiPctChg ~ s(sind, k=30, bs="cr") +
s(sind, by=occasional, k=30, bs = "cr") +
s(sind, by=daily, k=30, bs = "cr"),
data = right_0.pt.gam.df, method = "REML")
## Create a matrix of residuals
wi_gam.resid <- cbind(right_0.pt.gam.df[!(is.na(right_0.pt.gam.df$wiPctChg)),
c("subject_id", "frame")],
m.wi_gam$residuals)
names(wi_gam.resid) <- c("subject_id", "frame", "resid")
# wi_gam.resid$frame_char <- str_pad(wi_gam.resid$frame, width = 3, side = "left", pad = "0")
resid_mat <- reshape(wi_gam.resid[, c("subject_id", "frame", "resid")],
timevar = "frame",
idvar = "subject_id",
direction = "wide")
rownames(resid_mat) <- resid_mat$subject_id
resid_mat$subject_id <- NULL
# resid_names <- names(resid_mat)
# resid_names <- sort(resid_names)
# resid_mat <- resid_mat[, resid_names]
resid_mat <- as.matrix(resid_mat)
wi_gam.fpca <- fpca.face(resid_mat, knots = 15)
wi_gam.fpca$evalues/sum(wi_gam.fpca$evalues)## [1] 0.632623116 0.151378789 0.080436006 0.047798611 0.026647436 0.018360175
## [7] 0.015611564 0.010357839 0.007289294 0.005075438 0.004421731#Create data frame of eigen functions and attach to original data
Phi_mat <- as.data.frame(wi_gam.fpca$efunctions)
colnames(Phi_mat) <- paste0("Phi", seq(1, wi_gam.fpca$npc))
Phi_mat$frame <- as.numeric(rownames(Phi_mat)) -1
right_0.pt.gam.df <- merge(right_0.pt.gam.df, Phi_mat,
by = "frame",
all.x = T)
# right_0.pt.gam.df <- right_0.pt.gam.df[order(right_0.pt.gam.df$subject_id,
# right_0.pt.gam.df$frame), ]
right_0.pt.gam.df$subject_id <- as.factor(right_0.pt.gam.df$subject_id)
wi_gam.fri <- mgcv::bam(wiPctChg ~ s(sind, k=30, bs="cr") +
s(sind, by=occasional, k=30, bs = "cr") +
s(sind, by=daily, k=30, bs = "cr")+
s(subject_id, by = Phi1, bs="re") + s(subject_id, by = Phi2, bs="re")+
s(subject_id, by = Phi3, bs="re") + s(subject_id, by = Phi4, bs="re")+
s(subject_id, by = Phi5, bs="re") + s(subject_id, by = Phi6, bs="re"),
method = "fREML", data = right_0.pt.gam.df, discrete = TRUE)
summary(wi_gam.fri)##
## Family: gaussian
## Link function: identity
##
## Formula:
## wiPctChg ~ s(sind, k = 30, bs = "cr") + s(sind, by = occasional,
## k = 30, bs = "cr") + s(sind, by = daily, k = 30, bs = "cr") +
## s(subject_id, by = Phi1, bs = "re") + s(subject_id, by = Phi2,
## bs = "re") + s(subject_id, by = Phi3, bs = "re") + s(subject_id,
## by = Phi4, bs = "re") + s(subject_id, by = Phi5, bs = "re") +
## s(subject_id, by = Phi6, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.063 1.037 3.917 8.99e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(sind) 18.74 21.65 27.60 <2e-16 ***
## s(sind):occasional 18.80 21.91 23.24 <2e-16 ***
## s(sind):daily 19.53 22.64 31.40 <2e-16 ***
## s(subject_id):Phi1 81.64 84.00 32110.07 <2e-16 ***
## s(subject_id):Phi2 82.03 84.00 18773.35 <2e-16 ***
## s(subject_id):Phi3 82.79 84.00 5714.16 <2e-16 ***
## s(subject_id):Phi4 82.47 84.00 2915.36 <2e-16 ***
## s(subject_id):Phi5 82.26 84.00 451.05 <2e-16 ***
## s(subject_id):Phi6 82.70 84.00 852.09 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.954 Deviance explained = 95.5%
## fREML = 71439 Scale est. = 4.5204 n = 3210211.5.8 Plotting trajectory of occasional user with within person difference data
wi_gam.coef <- wi_gam.fri$coefficients
wi_gam.cov <- vcov.gam(wi_gam.fri)
df_pred_non <- data.frame("sind" = unique(right_0.pt.gam.df$sind), "occasional" = 0, "daily" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0, "Phi5" = 0, "Phi6" = 0,
"subject_id" = right_0.pt.gam.df$subject_id[1])
lpmat_non <- predict(wi_gam.fri, newdata=df_pred_non, se.fit=TRUE, type = "lpmatrix")
df_pred_occ <- data.frame("sind" = unique(right_0.pt.gam.df$sind), "occasional" = 1, "daily" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0, "Phi5" = 0, "Phi6" = 0,
"subject_id" = right_0.pt.gam.df$subject_id[1])
lpmat_occ <- predict(wi_gam.fri, newdata=df_pred_occ, se.fit=TRUE, type = "lpmatrix")
df_pred_dly <- data.frame("sind" = unique(right_0.pt.gam.df$sind), "occasional" = 0, "daily" = 1,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0, "Phi5" = 0, "Phi6" = 0,
"subject_id" = right_0.pt.gam.df$subject_id[1])
lpmat_dly <- predict(wi_gam.fri, newdata=df_pred_dly, se.fit=TRUE, type = "lpmatrix")
pred_df <- data.frame(seconds = rep(seq(0, 400), 3)/30,
user = c(rep("non-user", 401), rep("occasional", 401),rep("daily", 401)),
mean = c(lpmat_non %*% wi_gam.coef,
lpmat_occ %*% wi_gam.coef, lpmat_dly %*% wi_gam.coef),
se = c(sqrt(diag(lpmat_non %*% wi_gam.cov %*% t(lpmat_non))),
sqrt(diag(lpmat_occ %*% wi_gam.cov %*% t(lpmat_occ))),
sqrt(diag(lpmat_dly %*% wi_gam.cov %*% t(lpmat_dly)))),
stringsAsFactors = F)
# ggplot(data = pred_df, aes(x = frame, y = mean, group = user, col = user))+
# geom_line()+
# geom_line(aes(x=frame, y = mean + 2*se, group = user, col = user), linetype = "longdash")+
# geom_line(aes(x=frame, y = mean - 2*se, group = user, col = user), linetype = "longdash")+
# theme_bw()
wi_userProfile <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
scale_y_continuous(limits = c(min(pred_df$mean - 2*pred_df$se),
max(pred_df$mean+2*pred_df$se)))+
labs(y = "Within Person Difference in Percent Change")+
theme_bw()
legend_userProfile <- g_legend(wi_userProfile)
wi_userProfile.se <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
geom_line(aes(x=seconds, y = mean + 2*se, group = user, col = user),
linetype = "longdash")+
geom_line(aes(x=seconds, y = mean - 2*se, group = user, col = user),
linetype = "longdash")+
scale_y_continuous(limits = c(min(pred_df$mean - 2*pred_df$se),
max(pred_df$mean+2*pred_df$se)))+
labs(y = "")+
theme_bw()
wi_userTraj <- grid.arrange(arrangeGrob(wi_userProfile+theme(legend.position = "none"),
wi_userProfile.se+theme(legend.position = "none"), nrow = 1),
legend_userProfile, nrow = 1,
widths = c(30, 6))
11.5.9 Plotting average trajectories of users
pred_f1 <- predict(wi_gam.fri, newdata=df_pred_occ, se.fit=TRUE, type = "terms")
pred_f2 <- predict(wi_gam.fri, newdata=df_pred_dly, se.fit=TRUE, type = "terms")
pred_coef_df <- data.frame(seconds = seq(0, 400)/30,
f0_hat = lpmat_non %*% wi_gam.coef,
f0_se = sqrt(diag(lpmat_non %*% wi_gam.cov %*% t(lpmat_non))),
f1_hat = pred_f1$fit[, 2],
f1_se = pred_f1$se.fit[, 2],
f2_hat = pred_f2$fit[, 3],
f2_se = pred_f2$se.fit[, 3])
# ggplot(data = pred_coef_df, aes(x=frame, y = f0_hat))+
# geom_line()+
# geom_line(aes(x = frame, y = f0_hat + 2*f0_se), linetype = "longdash")+
# geom_line(aes(x = frame, y = f0_hat - 2*f0_se), linetype = "longdash")+
# labs(title = "Average Response of a Non-user", ylab = "Within Person Difference in Percent Change")+
# theme_bw()
wi_occ_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f1_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f1_hat + 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f1_hat - 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Within Person Difference (WPD): Occasional and Non-user",
y = "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
wi_dly_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f2_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f2_hat + 2*f2_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f2_hat - 2*f2_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Within Person Difference (WPD): Daily and Non-user",
y = "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
ylab <- textGrob(label = "Difference in WPD in Percent Change", rot = 90,
gp = gpar(fontsize = 12))
posval <- textGrob(label = sapply(strwrap("More WPD Constriction in User",
width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
negval <- textGrob(label = sapply(strwrap("Less WPD Constriction in User",
width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
wi_occ_coef <- grid.arrange(ylab, posval, negval, wi_occ_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))
wi_dly_coef <- grid.arrange(ylab, posval, negval, wi_dly_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))
11.5.10 Difference between Occasional and Non-user
This plot compares the difference between occasional and non-users for post and WPD data. In the post data there is significant positive difference in the occasional and non-users from frame 50 - 125 which would indicate that occasional user have less constriction during this time. In the WPD data there is a significant positive difference in the occasional and non-users from frame 10 - 175, indicating a greater difference in the difference between post and pre for the occasional vs non-user. Since the typical response has a positive difference between post and pre during this interval caused by less constriction at post compared to pre, we can say that there is even less constriction in post data of an occasional user compared to his/her pre than in a non-user.
11.5.11 Difference between Daily and Non-Users
These results are similar to the occassional vs non-user but less pronounced maybe indicating more variability in daily and non-users.
Differences in Within Person Difference between Daily and Occassional Users
11.5.12 Difference between Daily and Occasional Users
Difference in Within Person Difference Smoker vs Non-smokers
wi.smoker.gam.df <- merge(right_0.pt, pt.analytic.df,
by = c("subject_id", "user_cat"))
wi.smoker.gam.df$sind <- wi.smoker.gam.df$frame/400
wi.smoker.gam.df$smoker <- ifelse(wi.smoker.gam.df$user_cat == "non-user", 0, 1)
m.wi_smoker_gam <- mgcv::gam(wiPctChg ~ s(sind, k=15, bs="cr") +
s(sind, by=smoker, k=15, bs = "cr"),
data = wi.smoker.gam.df, method = "REML")
## Create a matrix of residuals
wi_smoker_gam.resid <- cbind(wi.smoker.gam.df[!(is.na(wi.smoker.gam.df$wiPctChg)),
c("subject_id", "frame")],
m.wi_smoker_gam$residuals)
names(wi_smoker_gam.resid) <- c("subject_id", "frame", "resid")
# wi_smoker_gam.resid$frame_char <- str_pad(wi_smoker_gam.resid$frame,
# width = 3, side = "left", pad = "0")
resid_mat <- reshape(wi_smoker_gam.resid[, c("subject_id", "frame", "resid")],
timevar = "frame",
idvar = "subject_id",
direction = "wide")
rownames(resid_mat) <- resid_mat$subject_id
resid_mat$subject_id <- NULL
# resid_names <- names(resid_mat)
# resid_names <- sort(resid_names)
# resid_mat <- resid_mat[, resid_names]
resid_mat <- as.matrix(resid_mat)
wi_smoker_gam.fpca <- fpca.face(resid_mat, knots = 15)
#Create data frame of eigen functions and attach to original data
Phi_mat <- as.data.frame(wi_smoker_gam.fpca$efunctions)
colnames(Phi_mat) <- paste0("Phi", seq(1, wi_smoker_gam.fpca$npc))
Phi_mat$frame <- as.numeric(rownames(Phi_mat)) -1
wi.smoker.gam.df <- merge(wi.smoker.gam.df, Phi_mat,
by = "frame",
all.x = T)
wi.smoker.gam.df <- smoker.gam.df[order(wi.smoker.gam.df$subject_id, wi.smoker.gam.df$frame), ]
wi.smoker.gam.df$subject_id <- as.factor(wi.smoker.gam.df$subject_id)
wi_smoker_gam.fri <- mgcv::bam(percent_change ~
s(sind, k=15, bs="cr") +
s(sind, by=smoker, k=15, bs = "cr") +
s(subject_id, by = Phi1, bs="re") + s(subject_id, by = Phi2, bs="re")+
s(subject_id, by = Phi3, bs="re") + s(subject_id, by = Phi4, bs="re"),
method = "fREML", data = wi.smoker.gam.df, discrete = TRUE)
summary(wi_smoker_gam.fri)##
## Family: gaussian
## Link function: identity
##
## Formula:
## percent_change ~ s(sind, k = 15, bs = "cr") + s(sind, by = smoker,
## k = 15, bs = "cr") + s(subject_id, by = Phi1, bs = "re") +
## s(subject_id, by = Phi2, bs = "re") + s(subject_id, by = Phi3,
## bs = "re") + s(subject_id, by = Phi4, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8011 1.0111 -0.792 0.428
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(sind) 13.42 13.72 208.8 <2e-16 ***
## s(sind):smoker 13.64 14.30 103.1 <2e-16 ***
## s(subject_id):Phi1 83.64 84.00 25787.8 <2e-16 ***
## s(subject_id):Phi2 82.48 84.00 8143.5 <2e-16 ***
## s(subject_id):Phi3 82.98 84.00 1724.7 <2e-16 ***
## s(subject_id):Phi4 81.92 84.00 3190.7 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.965 Deviance explained = 96.6%
## fREML = 63499 Scale est. = 2.6572 n = 32642Plot difference between smoker and non-smoker
wi_smoker_gam.coef <- wi_smoker_gam.fri$coefficients
wi_smoker_gam.cov <- vcov.gam(wi_smoker_gam.fri)
df_pred_non <- data.frame("sind" = sort(unique(wi.smoker.gam.df$sind)), "smoker" =0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = wi.smoker.gam.df$subject_id[1])
lpmat_non <- predict(wi_smoker_gam.fri, newdata=df_pred_non, se.fit=TRUE, type = "lpmatrix")
df_pred_smk <- data.frame("sind" = sort(unique(wi.smoker.gam.df$sind)), "smoker" = 1,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = wi.smoker.gam.df$subject_id[1])
lpmat_smk <- predict(wi_smoker_gam.fri, newdata=df_pred_smk, se.fit=TRUE, type = "lpmatrix")
pred_df <- data.frame(seconds = rep(seq(0, 400), 2)/30,
user = c(rep("non-user", 401), rep("smoker", 401)),
mean = c(lpmat_non %*% wi_smoker_gam.coef,
lpmat_smk %*% wi_smoker_gam.coef),
se = c(sqrt(diag(lpmat_non %*% wi_smoker_gam.cov %*% t(lpmat_non))),
sqrt(diag(lpmat_smk %*% wi_smoker_gam.cov %*% t(lpmat_smk)))),
stringsAsFactors = F)
# ggplot(data = pred_df, aes(x = frame, y = mean, group = user, col = user))+
# geom_line()+
# geom_line(aes(x=frame, y = mean + 2*se, group = user, col = user), linetype = "longdash")+
# geom_line(aes(x=frame, y = mean - 2*se, group = user, col = user), linetype = "longdash")+
# theme_bw()
wi_smkProfile <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
scale_y_continuous(limits = c(min(pred_df$mean - 2*pred_df$se),
max(pred_df$mean+2*pred_df$se)))+
labs(y = "Within Person Difference in Percent Change")+
theme_bw()
legend_smkProfile <- g_legend(wi_smkProfile)
wi_smkProfile.se <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
geom_line(aes(x=seconds, y = mean + 2*se, group = user, col = user),
linetype = "longdash")+
geom_line(aes(x=seconds, y = mean - 2*se, group = user, col = user),
linetype = "longdash")+
scale_y_continuous(limits = c(min(pred_df$mean - 2*pred_df$se),
max(pred_df$mean+2*pred_df$se)))+
labs(y = "")+
theme_bw()
wi_smk <- grid.arrange(arrangeGrob(wi_smkProfile+theme(legend.position = "none"),
wi_smkProfile.se+theme(legend.position = "none"), nrow = 1),
legend_smkProfile, nrow = 1,
widths = c(30, 6))
11.5.13 Average Trajectory of Smoker vs Non-Smoker
# pred_f0 <- predict(post_smoker_gam.fri, newdata=df_pred_non, se.fit=TRUE, type = "iterms")
pred_f1 <- predict(wi_smoker_gam.fri, newdata=df_pred_smk, se.fit=TRUE, type = "terms")
pred_coef_df <- data.frame(seconds = seq(0, 400)/30,
f0_hat = lpmat_non %*% wi_smoker_gam.coef,
f0_se = sqrt(diag(lpmat_non %*% wi_smoker_gam.cov %*% t(lpmat_non))),
f1_hat = pred_f1$fit[, 2],
f1_se = pred_f1$se.fit[, 2])
# ggplot(data = pred_coef_df, aes(x=frame, y = f0_hat))+
# geom_line()+
# geom_line(aes(x = frame, y = f0_hat + 2*f0_se), linetype = "longdash")+
# geom_line(aes(x = frame, y = f0_hat - 2*f0_se), linetype = "longdash")+
# labs(title = "Average Within Person Difference in Percent Change of a Non-user", ylab = "f0_hat")+
# theme_bw()
wi_smk_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f1_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f1_hat + 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f1_hat - 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Within Person Difference (WPD) in Percent Change \nbetween Smoker and Non-smoker",
y = "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
ylab <- textGrob(label = "Difference in WPD in Percent Change", rot = 90,
gp = gpar(fontsize = 12))
posval <- textGrob(label = sapply(strwrap("More WPD Constriction in User",
width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
negval <- textGrob(label = sapply(strwrap("Less WPD Constriction in User",
width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
wi_smk_coef <- grid.arrange(ylab, posval, negval, wi_smk_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))
11.5.14 Smoker vs Non-smoker Effect
## Time from consumption to post test
wi.consump.m <- lm(postConsumpTimeToTest ~ PC1 + PC2 + PC3 + PC4 + PC5 + PC6, data = pt.wi.scores)
summary(wi.consump.m)##
## Call:
## lm(formula = postConsumpTimeToTest ~ PC1 + PC2 + PC3 + PC4 +
## PC5 + PC6, data = pt.wi.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.9565 -3.5339 -0.7514 2.5591 18.6423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.910985 0.796032 77.775 <2e-16 ***
## PC1 0.004315 0.005670 0.761 0.450
## PC2 0.010014 0.011696 0.856 0.396
## PC3 0.022049 0.014884 1.481 0.145
## PC4 -0.002204 0.017981 -0.123 0.903
## PC5 -0.020716 0.025855 -0.801 0.427
## PC6 0.025848 0.032574 0.794 0.431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.581 on 48 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1067, Adjusted R-squared: -0.004993
## F-statistic: 0.9553 on 6 and 48 DF, p-value: 0.4653post.consump.m <- lm(postConsumpTimeToTest ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
summary(post.consump.m)##
## Call:
## lm(formula = postConsumpTimeToTest ~ PC1 + PC2 + PC3 + PC4, data = pt.scores)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.3643 -3.2540 -0.4397 2.7332 19.2217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.615735 0.792896 78.971 <2e-16 ***
## PC1 -0.007264 0.006659 -1.091 0.281
## PC2 0.029166 0.018235 1.599 0.116
## PC3 -0.022868 0.031685 -0.722 0.474
## PC4 0.025181 0.039465 0.638 0.526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.544 on 50 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.08165, Adjusted R-squared: 0.008178
## F-statistic: 1.111 on 4 and 50 DF, p-value: 0.3616pt.analytic.df <- merge(pt.analytic.df, pt.wi.scores[, c("subject_id", "PC1", "PC2", "PC3", "PC4", "PC5", "PC6")],
by = "subject_id")
names(pt.analytic.df)[names(pt.analytic.df) == "PC1"] <- "wpd.PC1"
names(pt.analytic.df)[names(pt.analytic.df) == "PC2"] <- "wpd.PC2"
names(pt.analytic.df)[names(pt.analytic.df) == "PC3"] <- "wpd.PC3"
names(pt.analytic.df)[names(pt.analytic.df) == "PC4"] <- "wpd.PC4"
names(pt.analytic.df)[names(pt.analytic.df) == "PC5"] <- "wpd.PC5"
names(pt.analytic.df)[names(pt.analytic.df) == "PC6"] <- "wpd.PC6"
pt.analytic.df <- merge(pt.analytic.df, pt.scores[, c("subject_id", "PC1", "PC2", "PC3", "PC4")],
by = "subject_id")
names(pt.analytic.df)[names(pt.analytic.df) == "PC1"] <- "post.PC1"
names(pt.analytic.df)[names(pt.analytic.df) == "PC2"] <- "post.PC2"
names(pt.analytic.df)[names(pt.analytic.df) == "PC3"] <- "post.PC3"
names(pt.analytic.df)[names(pt.analytic.df) == "PC4"] <- "post.PC4"
table1(~ postConsumpTimeToTest + Post_PreTime|user_cat,
data = pt.analytic.df)| non-user (N=29) |
occasional (N=30) |
daily (N=25) |
Overall (N=84) |
|
|---|---|---|---|---|
| postConsumpTimeToTest | ||||
| Mean (SD) | NA (NA) | 63.9 (6.26) | 60.2 (3.78) | 62.2 (5.57) |
| Median [Min, Max] | NA [NA, NA] | 63.5 [54.0, 84.0] | 60.0 [53.0, 67.0] | 62.0 [53.0, 84.0] |
| Missing | 29 (100%) | 0 (0%) | 0 (0%) | 29 (34.5%) |
| Post_PreTime | ||||
| Mean (SD) | 110 (8.90) | 116 (9.72) | 116 (6.58) | 114 (8.97) |
| Median [Min, Max] | 109 [99.0, 131] | 114 [104, 147] | 117 [106, 137] | 112 [99.0, 147] |
by(pt.analytic.df$postConsumpTimeToTest, INDICES = list(pt.analytic.df$user_cat), summary)## : non-user
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## NA NA NA NaN NA NA 29
## ------------------------------------------------------------
## : occasional
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 54.00 61.25 63.50 63.93 67.00 84.00
## ------------------------------------------------------------
## : daily
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 53.00 58.00 60.00 60.16 62.00 67.00postConsump.cor <- cor(pt.analytic.df$postConsumpTimeToTest,
pt.analytic.df[, c("demo_age", "demo_weight", "demo_height", "BMI",
"thc.post", "min_constriction.post", "slope.post",
"AUC.post", "thc_chg", "min_constriction_chg",
"AUC_chg", "wpd.PC1", "wpd.PC2", "wpd.PC3",
"wpd.PC4", "wpd.PC5", "wpd.PC6",
"post.PC1", "post.PC2", "post.PC3", "post.PC4",
"vas1_high_score", "vas_high_score_chg",
"vas1_score_dc", "vas_score_dc_chg",
"Post_PreTime")],
use = "pairwise.complete.obs")
postConsump.rcorr <- rcorr(as.matrix(pt.analytic.df[, c("postConsumpTimeToTest",
"demo_age", "demo_weight",
"demo_height", "BMI",
"thc.post", "min_constriction.post",
"slope.post", "AUC.post",
"thc_chg", "min_constriction_chg",
"AUC_chg",
"wpd.PC1", "wpd.PC2", "wpd.PC3",
"wpd.PC4", "wpd.PC5", "wpd.PC6",
"post.PC1", "post.PC2",
"post.PC3", "post.PC4",
"vas1_high_score",
"vas_high_score_chg",
"vas1_score_dc",
"vas_score_dc_chg",
"Post_PreTime")]))
postConsump.cor.df <- data.frame(postConsump_rho = postConsump.rcorr$r[, 1],
postConsump_p = postConsump.rcorr$P[, 1],
postConsump_n = postConsump.rcorr$n[, 1])
postConsump.cor.df <- postConsump.cor.df[order(-1*abs(postConsump.cor.df$postConsump_rho)), ]
postConsump.cor.df <- postConsump.cor.df[-1, ]
rm(postConsump.rcorr)
knitr::kable(postConsump.cor.df)| postConsump_rho | postConsump_p | postConsump_n | |
|---|---|---|---|
| Post_PreTime | 0.3894282 | 0.0032958 | 55 |
| min_constriction.post | -0.2167006 | 0.1120314 | 55 |
| post.PC2 | 0.2105140 | 0.1229040 | 55 |
| wpd.PC3 | 0.2014399 | 0.1402745 | 55 |
| vas1_high_score | 0.1823385 | 0.1827289 | 55 |
| vas_high_score_chg | 0.1814208 | 0.1849790 | 55 |
| AUC_chg | 0.1676010 | 0.2212969 | 55 |
| wpd.PC6 | 0.1653819 | 0.2275616 | 55 |
| min_constriction_chg | 0.1476973 | 0.2818785 | 55 |
| post.PC1 | -0.1419379 | 0.3012731 | 55 |
| wpd.PC5 | -0.1410364 | 0.3043854 | 55 |
| vas_score_dc_chg | -0.1409231 | 0.3047780 | 55 |
| vas1_score_dc | -0.1393192 | 0.3103704 | 55 |
| wpd.PC1 | 0.1380900 | 0.3147006 | 55 |
| slope.post | 0.1323212 | 0.3355358 | 55 |
| BMI | -0.1285025 | 0.3497909 | 55 |
| post.PC3 | -0.1163836 | 0.3974525 | 55 |
| thc.post | -0.1124720 | 0.4180944 | 54 |
| thc_chg | -0.1094518 | 0.4307816 | 54 |
| wpd.PC2 | 0.1082799 | 0.4313412 | 55 |
| demo_weight | -0.1043995 | 0.4481278 | 55 |
| AUC.post | -0.0775811 | 0.5734411 | 55 |
| demo_age | 0.0601910 | 0.6624510 | 55 |
| wpd.PC4 | -0.0401901 | 0.7707995 | 55 |
| demo_height | -0.0133431 | 0.9229754 | 55 |
| post.PC4 | 0.0121542 | 0.9298203 | 55 |
ggplot(data = pt.analytic.df[pt.analytic.df$user_cat != "non-user", ], aes(x = user_cat, y = postConsumpTimeToTest, col = user_cat))+
geom_boxplot()+
theme_bw()
ggplot(data = pt.analytic.df[pt.analytic.df$user_cat != "non-user", ], aes(x = demo_gender, y = postConsumpTimeToTest, col = demo_gender))+
geom_boxplot()+
theme_bw()
ggplot(data = pt.analytic.df[pt.analytic.df$user_cat != "non-user", ], aes(x = postConsumpTimeToTest, y = Post_PreTime, col = user_cat))+
geom_point()+
theme_bw()
ggplot(data = pt.analytic.df[pt.analytic.df$user_cat != "non-user", ], aes(x = postConsumpTimeToTest, y = min_constriction.post, col = user_cat))+
geom_point()+
theme_bw()
ggplot(data = pt.analytic.df[pt.analytic.df$user_cat != "non-user", ], aes(x = postConsumpTimeToTest, y = post.PC2, col = user_cat))+
geom_point()+
theme_bw()
ggplot(data = pt.analytic.df[pt.analytic.df$user_cat != "non-user", ], aes(x = postConsumpTimeToTest, y = wpd.PC3, col = user_cat))+
geom_point()+
theme_bw()
11.6 SoFR - prediction of smoker vs non-smoker
smk.df <- pt.analytic.df[, c("subject_id", "user_cat")]
smk.df$smoker <- ifelse(pt.analytic.df$user_cat %in% c("daily", "occasional"), 1, 0)
# Zsm <- post_right_0.fpca$Yhat
pctChg_names <- names(right_0.post.w)[4:379]
sofr_right_post <- right_0.post.w[, pctChg_names]
sind <- seq(0, 1, len = 376)
smat <- matrix(sind, nrow(smk.df), 376, byrow = T)
# smk.df$Zsm <- I(Zsm)
smk.df$Zraw <- I(as.matrix(sofr_right_post))
smk.df$smat <- I(smat)
smk.df$lmat <- I(matrix(1/376, nrow(smk.df), 376))
smk.df$zlmat <- I(smk.df$lmat * smk.df$Zraw)
smk_fglm_ps <- gam(smoker ~ s(smat, by=zlmat, bs = "cr", k = 30), data=smk.df,
method = "REML", family = binomial)
summary(smk_fglm_ps)##
## Family: binomial
## Link function: logit
##
## Formula:
## smoker ~ s(smat, by = zlmat, bs = "cr", k = 30)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9696 0.6172 1.571 0.116
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(smat):zlmat 2.601 2.93 2.347 0.424
##
## R-sq.(adj) = 0.0198 Deviance explained = 4.09%
## -REML = 45.271 Scale est. = 1 n = 71roc_in_sample <- roc(smk_fglm_ps$model$smoker, smk_fglm_ps$fitted.values)## Setting levels: control = 0, case = 1## Setting direction: controls < casesauc(roc_in_sample)## Area under the curve: 0.6383df_sofr <- data.frame("smat" = sind)11.7 VAS based “high” vs “not in occasional smokers
hist(pt.analytic.df$vas1_high_score[pt.analytic.df$user_cat == "daily"], breaks = 15)
hist(pt.analytic.df$vas1_high_score[pt.analytic.df$user_cat == "occasional"], breaks = 15)
boxplot(vas1_high_score ~ user_cat, data = pt.analytic.df)
summary(pt.analytic.df$vas1_high_score[pt.analytic.df$user_cat == "daily"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.00 42.00 46.00 47.24 57.00 78.00summary(pt.analytic.df$vas1_high_score[pt.analytic.df$user_cat == "occasional"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.00 37.25 51.25 48.23 61.12 79.00by(pt.analytic.df$vas1_high_score, INDICES = pt.analytic.df$user_cat, FUN = summary)## pt.analytic.df$user_cat: non-user
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
## ------------------------------------------------------------
## pt.analytic.df$user_cat: occasional
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.00 37.25 51.25 48.23 61.12 79.00
## ------------------------------------------------------------
## pt.analytic.df$user_cat: daily
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.00 42.00 46.00 47.24 57.00 78.00sum(pt.analytic.df$vas1_high_score[pt.analytic.df$user_cat == "occasional"] >= 46, na.rm = T)## [1] 17sum(pt.analytic.df$vas1_high_score[pt.analytic.df$user_cat == "occasional"] < 46, na.rm = T)## [1] 13pt.analytic.df$vas_high <- NA
pt.analytic.df$vas_high[pt.analytic.df$vas1_high_score >= 46 &
pt.analytic.df$user_cat == "occasional"] <- 1
pt.analytic.df$vas_high[pt.analytic.df$vas1_high_score < 46 &
pt.analytic.df$user_cat == "occasional"] <- 011.7.1 VAS FoSR
right_0.vas.df <- merge(right_0.post, pt.analytic.df[!(is.na(pt.analytic.df$vas_high)), ],
by = c("subject_id", "user_cat"))
right_0.vas.df <- right_0.vas.df[order(right_0.vas.df$subject_id, right_0.vas.df$frame), ]
right_0.vas.df$sind <- right_0.vas.df$frame/400
m.post_vas <- mgcv::gam(percent_change ~ s(sind, k=30, bs="cr") +
s(sind, by=vas_high, k=30, bs = "cr"),
data = right_0.vas.df, method = "REML")
## Create a matrix of residuals
post_vas.resid <- cbind(right_0.vas.df[!(is.na(right_0.vas.df$percent_change)),
c("subject_id", "frame")],
m.post_vas$residuals)
names(post_vas.resid) <- c("subject_id", "frame", "resid")
post_vas.resid$frame_char <- str_pad(post_vas.resid$frame, width = 3, side = "left", pad = "0")
resid_mat <- reshape(post_vas.resid[, c("subject_id", "frame_char", "resid")],
timevar = "frame_char",
idvar = "subject_id",
direction = "wide")
rownames(resid_mat) <- resid_mat$subject_id
resid_mat$subject_id <- NULL
resid_names <- names(resid_mat)
resid_names <- sort(resid_names)
resid_mat <- resid_mat[, resid_names]
resid_mat <- as.matrix(resid_mat)
post_vas.fpca <- fpca.face(resid_mat, knots = 15)
#Create data frame of eigen functions and attach to original data
Phi_mat <- as.data.frame(post_vas.fpca$efunctions)
colnames(Phi_mat) <- paste0("Phi", seq(1, post_vas.fpca$npc))
Phi_mat$frame <- as.numeric(rownames(Phi_mat)) -1
right_0.vas.df <- merge(right_0.vas.df, Phi_mat,
by = "frame",
all.x = T)
right_0.vas.df <- right_0.vas.df[order(right_0.vas.df$subject_id, right_0.vas.df$frame), ]
right_0.vas.df$subject_id <- as.factor(right_0.vas.df$subject_id)
post_vas.fri <- mgcv::bam(percent_change ~
s(sind, k=30, bs="cr") +
s(sind, by=vas_high, k=30, bs = "cr") +
s(subject_id, by = Phi1, bs="re") + s(subject_id, by = Phi2, bs="re")+
s(subject_id, by = Phi3, bs="re") + s(subject_id, by = Phi4, bs="re"),
method = "fREML", data = right_0.vas.df, discrete = TRUE)
summary(post_vas.fri)##
## Family: gaussian
## Link function: identity
##
## Formula:
## percent_change ~ s(sind, k = 30, bs = "cr") + s(sind, by = vas_high,
## k = 30, bs = "cr") + s(subject_id, by = Phi1, bs = "re") +
## s(subject_id, by = Phi2, bs = "re") + s(subject_id, by = Phi3,
## bs = "re") + s(subject_id, by = Phi4, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.961 1.046 -7.608 2.99e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(sind) 24.53 26.55 40.798 <2e-16 ***
## s(sind):vas_high 19.26 22.39 6.104 <2e-16 ***
## s(subject_id):Phi1 28.97 30.00 27400.457 <2e-16 ***
## s(subject_id):Phi2 28.80 30.00 6461.315 <2e-16 ***
## s(subject_id):Phi3 28.87 30.00 2011.017 <2e-16 ***
## s(subject_id):Phi4 29.11 30.00 1262.366 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.97 Deviance explained = 97%
## fREML = 19574 Scale est. = 1.6192 n = 1149611.7.2 Plotting trajectory of occasional & daily user
post_vas.coef <- post_vas.fri$coefficients
post_vas.cov <- vcov.gam(post_vas.fri)
df_pred_non <- data.frame("sind" = unique(right_0.vas.df$sind), "vas_high" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = right_0.vas.df$subject_id[1])
lpmat_non <- predict(post_vas.fri, newdata=df_pred_non, se.fit=TRUE, type = "lpmatrix")
df_pred_high <- data.frame("sind" = unique(right_0.vas.df$sind), "vas_high" = 1,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
"Phi4" = 0,
"subject_id" = right_0.vas.df$subject_id[1])
lpmat_high <- predict(post_vas.fri, newdata=df_pred_high, se.fit=TRUE, type = "lpmatrix")
pred_df <- data.frame(seconds = rep(seq(0, 400), 2)/30,
user = c(rep(0, 401), rep(1, 401)),
mean = c(lpmat_non %*% post_vas.coef,
lpmat_high %*% post_vas.coef),
se = c(sqrt(diag(lpmat_non %*% post_vas.cov %*% t(lpmat_non))),
sqrt(diag(lpmat_high %*% post_vas.cov %*% t(lpmat_high)))),
stringsAsFactors = F)
pred_df$user <- factor(pred_df$user,
levels = c(0, 1),
labels = c("not high", "high"))
post_vasProfile <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
labs(y = "Percent Change")+
theme_bw()
legend_vasProfile <- g_legend(post_vasProfile)
post_vasProfile.se <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
geom_line(aes(x=seconds, y = mean + 2*se, group = user, col = user),
linetype = "longdash")+
geom_line(aes(x=seconds, y = mean - 2*se, group = user, col = user),
linetype = "longdash")+
labs(y = "")+
theme_bw()
post_vasTraj <- grid.arrange(arrangeGrob(post_vasProfile+theme(legend.position = "none"),
post_vasProfile.se+theme(legend.position = "none"),
nrow = 1),
legend_vasProfile, nrow = 1,
widths = c(30, 6))
11.7.3 Plot vas high vs not occassional users
right_0.vas.df$vas_high <- factor(right_0.vas.df$vas_high,
levels = c(0,1),
labels = c("not high", "high"))
ggplot(right_0.vas.df, aes(x=seconds, y = percent_change, group = subject_id,
col = vas_high))+
geom_line()+
geom_line(data = pred_df[pred_df$user == "not high",],
aes(x=seconds, y = mean, group = user), color = "darkred", size = 1.2)+
geom_line(data = pred_df[pred_df$user == "high",],
aes(x=seconds, y = mean, group = user), color = "darkblue", size = 1.2)+
theme_bw()## Warning: Removed 534 row(s) containing missing values (geom_path).
11.7.4 Effect for VAS “high” vs “not”
pred_f1 <- predict(post_vas.fri, newdata=df_pred_high, se.fit=TRUE, type = "terms")
pred_coef_df <- data.frame(seconds = seq(0, 400)/30,
f1_hat = pred_f1$fit[, 2],
f1_se = pred_f1$se.fit[, 2])
post_high_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f1_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f1_hat + 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f1_hat - 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Percent Change: VAS high vs not (Post)", ylab = "Percent Change")+
theme_bw()
ylab <- textGrob(label = "Difference in Percent Change", rot = 90,
gp = gpar(fontsize = 12))
posval <- textGrob(label = sapply(strwrap("Less Constriction in User", width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
negval <- textGrob(label = sapply(strwrap("More Constriction in User", width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
post_high_coef <- grid.arrange(ylab, posval, negval, post_high_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))
post_high_coef## TableGrob (2 x 17) "arrange": 4 grobs
## z cells name grob
## 1 1 ( 1- 2, 1- 1) arrange text[GRID.text.4204]
## 2 2 ( 1- 1, 2- 2) arrange text[GRID.text.4205]
## 3 3 ( 2- 2, 2- 2) arrange text[GRID.text.4206]
## 4 4 ( 1- 2, 3-17) arrange gtable[layout]11.8 Correlation between left and & right eyes - post
right_0.post.w2 <- right_0.post.w
right.names <- names(right_0.post.w2)
right.names <- c(right.names[1:3], paste0("R_", right.names[4:(length(right.names)-1)]), right.names[length(right.names)])
names(right_0.post.w2) <- right.names
left_0.post.w2 <- left_0.post.w
left.names <- names(left_0.post.w2)
left.names <- c(left.names[1:3], paste0("L_", left.names[4:(length(left.names)-1)]), left.names[length(left.names)])
names(left_0.post.w2) <- left.names
botheyes <- merge(right_0.post.w2, left_0.post.w2,
by = c("subject_id", "tp", "user_cat"))
right.corr.names <- right.names[4:(length(right.names)-1)]
left.corr.names <- left.names[4:(length(left.names)-1)]
botheye.corr <- round(cor(botheyes[, right.corr.names[-1]],
botheyes[, left.corr.names[-1]], use = "pairwise.complete.obs"), 3)
corr_df <- data.frame(frame = seq(1, 400, by = 1),
btwEyeCor = diag(botheye.corr))
# get_upper_tri <- function(cormat){
# cormat[lower.tri(cormat)]<- NA
# return(cormat)
# }
#
# upper_tri <- get_upper_tri(botheye.corr)
# library(reshape2)
#
# melted_cor_mat <- melt(botheye.corr)
# names(melted_cor_mat) <- c("RightEye", "LeftEye", "value")
#
# jpeg(file.path(res_folder, "Corr_Left_Right_Eyes.jpg"),
# height = 6, width = 15, res = 300, units = "in")
# ggplot(data = melted_cor_mat, aes(RightEye, LeftEye, fill = value))+
# geom_tile()+
# scale_fill_gradient2(low = "blue", high = "red", mid = "white",
# midpoint = 0, limit = c(-0.2,1), space = "Lab",
# name="Pearson\nCorrelation") +
# theme_bw()+
# theme(axis.text.x = element_blank(),
# axis.text.y = element_blank())+
# coord_fixed()
# dev.off()
ggplot(data = corr_df, aes(frame, btwEyeCor))+
geom_point()+
scale_y_continuous(limits = c(0,1))+
labs(title = "Correlation between eyes -- POST")+
theme_bw()
11.9 Correlation between left and & right eyes - wpd
right_0.pt.w2 <- right_0.pt.w
right.names <- names(right_0.pt.w2)
right.names <- c(right.names[1:2], paste0("R_", right.names[3:(length(right.names)-1)]), right.names[length(right.names)])
names(right_0.pt.w2) <- right.names
left_0.pt.w2 <- left_0.pt.w
left.names <- names(left_0.pt.w2)
left.names <- c(left.names[1:2], paste0("L_", left.names[3:(length(left.names)-1)]), left.names[length(left.names)])
names(left_0.pt.w2) <- left.names
wpd_botheyes <- merge(right_0.pt.w2, left_0.pt.w2,
by = c("subject_id", "user_cat"))
right.corr.names <- right.names[3:(length(right.names)-1)]
left.corr.names <- left.names[3:(length(left.names)-1)]
wpd_botheye.corr <- round(cor(wpd_botheyes[, right.corr.names],
wpd_botheyes[, left.corr.names],
use = "pairwise.complete.obs"), 3)
wpd_corr_df <- data.frame(frame = seq(1, 400, by = 1),
btwEyeCor = diag(wpd_botheye.corr))
ggplot(data = wpd_corr_df, aes(frame, btwEyeCor))+
geom_point()+
scale_y_continuous(limits = c(0,1))+
labs(title = "Correlation between eyes -- WPD")+
theme_bw()
11.10 Function on Scalar Regression
11.10.1 Post – Interaction with Post Consumption Time to Test (PCTT)
\[ \begin{aligned} Y_{i}(t) &= f_0(t) + f_1(t)*I(user = occasional) + f_2(t)*I(user = daily) +f_3(t)*I(user = occasional)*PCTT + f_4(t)*I(user = daily)*PCTT + b_i(t) + \epsilon_i(t)\\ & b_i(t) \sim GP(0, \Sigma_b) \\ & \epsilon_i(t) \sim N(0, \sigma_{\epsilon}^2) \\ &= \sum_{k=1}^K \xi_{0k}\phi_{0k}(t_i) + \sum_{k=1}^K \xi_{1k}\phi_{1k}(t_i)*I(user = occasional) + \sum_{k=1}^K \xi_{2k}\phi_{2k}(t_i)*I(user = daily)\\ &+ \sum_{k=1}^K \xi_{3k}\phi_{3k}(t_i)*I(user = occasional)*PCTT + \sum_{k=1}^K \xi_{4k}\phi_{4k}(t_i)*I(user = daily)*PCTT + \sum_{k=1}^K \xi_{ik}\phi_k(t_i) + \epsilon_i(t)\\ \end{aligned} \]
pt.analytic.df$postConsumpTimeToTest_c <- pt.analytic.df$postConsumpTimeToTest- round(mean(pt.analytic.df$postConsumpTimeToTest, na.rm = T), 3)
pt.analytic.df$occ_PCTT <- pt.analytic.df$occasional*pt.analytic.df$postConsumpTimeToTest_c
pt.analytic.df$dly_PCTT <- pt.analytic.df$daily*pt.analytic.df$postConsumpTimeToTest_c
pt.analytic.df$occ_PCTT[pt.analytic.df$user == "non-user"] <- 0
pt.analytic.df$dly_PCTT[pt.analytic.df$user == "non-user"] <- 0
post_intPCTT.gam.df <- merge(right_0.post, pt.analytic.df,
by = c("subject_id", "user_cat"))
# right_0.gam.df <- right_0.gam.df[order(right_0.gam.df$subject_id, right_0.gam.df$frame), ]
post_intPCTT.gam.df$sind <- post_intPCTT.gam.df$frame/400
# head(right_0.gam.df)
m.post_intPCTT_gam <- mgcv::bam(percent_change ~ s(sind, k=30, bs="cr") +
s(sind, by=occasional, k=30, bs = "cr") +
s(sind, by=daily, k=30, bs = "cr") +
s(sind, by=occ_PCTT, k=30, bs = "cr") +
s(sind, by=dly_PCTT, k=30, bs = "cr"),
data = post_intPCTT.gam.df, method = "REML")
summary(m.post_intPCTT_gam)##
## Family: gaussian
## Link function: identity
##
## Formula:
## percent_change ~ s(sind, k = 30, bs = "cr") + s(sind, by = occasional,
## k = 30, bs = "cr") + s(sind, by = daily, k = 30, bs = "cr") +
## s(sind, by = occ_PCTT, k = 30, bs = "cr") + s(sind, by = dly_PCTT,
## k = 30, bs = "cr")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12.25464 0.07029 -174.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(sind) 22.077 25.48 201.94 <2e-16 ***
## s(sind):occasional 10.206 12.42 47.48 <2e-16 ***
## s(sind):daily 9.719 11.83 24.03 <2e-16 ***
## s(sind):occ_PCTT 11.555 14.08 65.76 <2e-16 ***
## s(sind):dly_PCTT 8.630 10.51 12.11 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.267 Deviance explained = 26.8%
## -REML = 1.1209e+05 Scale est. = 55.955 n = 32642## Create a matrix of residuals
post_intPCTT_gam.resid <- cbind(post_intPCTT.gam.df[!(is.na(post_intPCTT.gam.df$percent_change)),
c("subject_id", "frame")],
m.post_intPCTT_gam$residuals)
names(post_intPCTT_gam.resid) <- c("subject_id", "frame", "resid")
# post_gam.resid$frame_char <- str_pad(post_gam.resid$frame, width = 3, side = "left", pad = "0")
resid_mat <- reshape(post_intPCTT_gam.resid[, c("subject_id", "frame", "resid")],
timevar = "frame",
idvar = "subject_id",
direction = "wide")
rownames(resid_mat) <- resid_mat$subject_id
resid_mat$subject_id <- NULL
# resid_names <- names(resid_mat)
# resid_names <- sort(resid_names)
# resid_mat <- resid_mat[, resid_names]
resid_mat <- as.matrix(resid_mat)
post_intPCTT_gam.fpca <- fpca.face(resid_mat, knots = 15)
#Create data frame of eigen functions and attach to original data
Phi_mat <- as.data.frame(post_gam.fpca$efunctions)
colnames(Phi_mat) <- paste0("Phi", seq(1, post_gam.fpca$npc))
Phi_mat$frame <- as.numeric(rownames(Phi_mat)) -1
post_intPCTT.gam.df <- merge(post_intPCTT.gam.df, Phi_mat,
by = "frame",
all.x = T)
post_intPCTT.gam.df <- post_intPCTT.gam.df[order(post_intPCTT.gam.df$subject_id, post_intPCTT.gam.df$frame), ]
post_intPCTT.gam.df$subject_id <- as.factor(post_intPCTT.gam.df$subject_id)
post_intPCTT_gam.fri <- mgcv::bam(percent_change ~
s(sind, k=30, bs="cr") +
s(sind, by=occasional, k=30, bs = "cr") +
s(sind, by=daily, k=30, bs = "cr") +
s(sind, by=occ_PCTT, k=30, bs = "cr") +
s(sind, by=dly_PCTT, k=30, bs = "cr")+
s(subject_id, by = Phi1, bs="re") + s(subject_id, by = Phi2, bs="re")+
s(subject_id, by = Phi3, bs="re") + s(subject_id, by = Phi4, bs="re"),
method = "fREML", data = post_intPCTT.gam.df, discrete = TRUE)
summary(post_intPCTT_gam.fri)##
## Family: gaussian
## Link function: identity
##
## Formula:
## percent_change ~ s(sind, k = 30, bs = "cr") + s(sind, by = occasional,
## k = 30, bs = "cr") + s(sind, by = daily, k = 30, bs = "cr") +
## s(sind, by = occ_PCTT, k = 30, bs = "cr") + s(sind, by = dly_PCTT,
## k = 30, bs = "cr") + s(subject_id, by = Phi1, bs = "re") +
## s(subject_id, by = Phi2, bs = "re") + s(subject_id, by = Phi3,
## bs = "re") + s(subject_id, by = Phi4, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.1111 0.9229 -10.96 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(sind) 27.53 28.42 119.05 <2e-16 ***
## s(sind):occasional 18.76 22.06 77.86 <2e-16 ***
## s(sind):daily 18.16 21.43 38.47 <2e-16 ***
## s(sind):occ_PCTT 21.94 25.24 28.26 <2e-16 ***
## s(sind):dly_PCTT 19.38 22.77 17.82 <2e-16 ***
## s(subject_id):Phi1 81.24 84.00 22076.91 <2e-16 ***
## s(subject_id):Phi2 80.29 84.00 2495.67 <2e-16 ***
## s(subject_id):Phi3 81.38 84.00 787.75 <2e-16 ***
## s(subject_id):Phi4 79.63 84.00 1027.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.967 Deviance explained = 96.7%
## fREML = 62846 Scale est. = 2.5436 n = 3264211.10.2 Plotting trajectory of occasional & daily user
post_intPCTT_gam.coef <- post_intPCTT_gam.fri$coefficients
post_intPCTT_gam.cov <- vcov.gam(post_intPCTT_gam.fri)
df_pred_non <- data.frame("sind" = unique(post_intPCTT.gam.df$sind), "occasional" = 0, "daily" = 0, "occ_PCTT" = 0, "dly_PCTT" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0, "Phi4" = 0,
"subject_id" = post_intPCTT.gam.df$subject_id[1])
lpmat_non <- predict(post_intPCTT_gam.fri, newdata=df_pred_non, se.fit=TRUE, type = "lpmatrix")
df_pred_occ <- data.frame("sind" = unique(post_intPCTT.gam.df$sind), "occasional" = 1, "daily" = 0, "occ_PCTT" = 0, "dly_PCTT" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0, "Phi4" = 0,
"subject_id" = post_intPCTT.gam.df$subject_id[1])
lpmat_occ <- predict(post_intPCTT_gam.fri, newdata=df_pred_occ, se.fit=TRUE, type = "lpmatrix")
df_pred_occP10 <- data.frame("sind" = unique(post_intPCTT.gam.df$sind), "occasional" = 1, "daily" = 0, "occ_PCTT" = 10, "dly_PCTT" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0, "Phi4" = 0,
"subject_id" = post_intPCTT.gam.df$subject_id[1])
lpmat_occP10 <- predict(post_intPCTT_gam.fri, newdata=df_pred_occP10, se.fit=TRUE, type = "lpmatrix")
df_pred_occM10 <- data.frame("sind" = unique(post_intPCTT.gam.df$sind), "occasional" = 1, "daily" = 0, "occ_PCTT" = -10, "dly_PCTT" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0, "Phi4" = 0,
"subject_id" = post_intPCTT.gam.df$subject_id[1])
lpmat_occM10 <- predict(post_intPCTT_gam.fri, newdata=df_pred_occM10, se.fit=TRUE, type = "lpmatrix")
# df_pred_dly <- data.frame("sind" = unique(right_0.gam.df$sind), "occasional" = 0, "daily" = 1,
# "Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
# "Phi4" = 0,
# "subject_id" = right_0.gam.df$subject_id[1])
#
# lpmat_dly <- predict(post_gam.fri, newdata=df_pred_dly, se.fit=TRUE, type = "lpmatrix")
pred_df <- data.frame(seconds = rep(seq(0, 400), 4)/30,
user = c(rep("non-user", 401), rep("occasional", 401), rep("occ+10", 401), rep("occ-10", 401)
# rep("daily", 401)
),
mean = c(lpmat_non %*% post_intPCTT_gam.coef,
lpmat_occ %*% post_intPCTT_gam.coef,
lpmat_occP10 %*% post_intPCTT_gam.coef,
lpmat_occM10 %*% post_intPCTT_gam.coef
# lpmat_dly %*% post_gam.coef
),
se = c(sqrt(diag(lpmat_non %*% post_intPCTT_gam.cov %*% t(lpmat_non))),
sqrt(diag(lpmat_occ %*% post_intPCTT_gam.cov %*% t(lpmat_occ))),
sqrt(diag(lpmat_occP10 %*% post_intPCTT_gam.cov %*% t(lpmat_occP10))),
sqrt(diag(lpmat_occM10 %*% post_intPCTT_gam.cov %*% t(lpmat_occM10)))
# sqrt(diag(lpmat_dly %*% post_gam.cov %*% t(lpmat_dly)))
),
stringsAsFactors = F)
post_userProfile <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
labs(y = "Percent Change")+
theme_bw()
legend_userProfile <- g_legend(post_userProfile)
post_userProfile.se <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
geom_line(aes(x=seconds, y = mean + 2*se, group = user, col = user),
linetype = "longdash")+
geom_line(aes(x=seconds, y = mean - 2*se, group = user, col = user),
linetype = "longdash")+
labs(y = "")+
theme_bw()
post_userTraj <- grid.arrange(arrangeGrob(post_userProfile+theme(legend.position = "none"),
post_userProfile.se+theme(legend.position = "none"), nrow = 1),
legend_userProfile, nrow = 1,
widths = c(30, 6))
# post_intPCTT_gam.coef <- post_intPCTT_gam.fri$coefficients
# post_intPCTT_gam.cov <- vcov.gam(post_intPCTT_gam.fri)
#
# df_pred_non <- data.frame("sind" = unique(post_intPCTT.gam.df$sind), "occasional" = 0, "daily" = 0, "occ_PCTT" = 0, "dly_PCTT" = 0,
# "Phi1" = 0, "Phi2" = 0, "Phi3" = 0, "Phi4" = 0,
# "subject_id" = post_intPCTT.gam.df$subject_id[1])
#
# lpmat_non <- predict(post_intPCTT_gam.fri, newdata=df_pred_non, se.fit=TRUE, type = "lpmatrix")
df_pred_dly <- data.frame("sind" = unique(post_intPCTT.gam.df$sind), "occasional" = 0, "daily" = 1, "occ_PCTT" = 0, "dly_PCTT" = 0,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0, "Phi4" = 0,
"subject_id" = post_intPCTT.gam.df$subject_id[1])
lpmat_dly <- predict(post_intPCTT_gam.fri, newdata=df_pred_dly, se.fit=TRUE, type = "lpmatrix")
df_pred_dlyP10 <- data.frame("sind" = unique(post_intPCTT.gam.df$sind), "occasional" = 0, "daily" = 1, "occ_PCTT" = 0, "dly_PCTT" = 10,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0, "Phi4" = 0,
"subject_id" = post_intPCTT.gam.df$subject_id[1])
lpmat_dlyP10 <- predict(post_intPCTT_gam.fri, newdata=df_pred_dlyP10, se.fit=TRUE, type = "lpmatrix")
df_pred_dlyM10 <- data.frame("sind" = unique(post_intPCTT.gam.df$sind), "occasional" = 0, "daily" = 1, "occ_PCTT" = 0, "dly_PCTT" = -10,
"Phi1" = 0, "Phi2" = 0, "Phi3" = 0, "Phi4" = 0,
"subject_id" = post_intPCTT.gam.df$subject_id[1])
lpmat_dlyM10 <- predict(post_intPCTT_gam.fri, newdata=df_pred_dlyM10, se.fit=TRUE, type = "lpmatrix")
# df_pred_dly <- data.frame("sind" = unique(right_0.gam.df$sind), "occasional" = 0, "daily" = 1,
# "Phi1" = 0, "Phi2" = 0, "Phi3" = 0,
# "Phi4" = 0,
# "subject_id" = right_0.gam.df$subject_id[1])
#
# lpmat_dly <- predict(post_gam.fri, newdata=df_pred_dly, se.fit=TRUE, type = "lpmatrix")
pred_df <- data.frame(seconds = rep(seq(0, 400), 4)/30,
user = c(rep("non-user", 401), rep("daily", 401), rep("dly+10", 401), rep("dly-10", 401)
# rep("daily", 401)
),
mean = c(lpmat_non %*% post_intPCTT_gam.coef,
lpmat_dly %*% post_intPCTT_gam.coef,
lpmat_dlyP10 %*% post_intPCTT_gam.coef,
lpmat_dlyM10 %*% post_intPCTT_gam.coef
# lpmat_dly %*% post_gam.coef
),
se = c(sqrt(diag(lpmat_non %*% post_intPCTT_gam.cov %*% t(lpmat_non))),
sqrt(diag(lpmat_dly %*% post_intPCTT_gam.cov %*% t(lpmat_dly))),
sqrt(diag(lpmat_dlyP10 %*% post_intPCTT_gam.cov %*% t(lpmat_dlyP10))),
sqrt(diag(lpmat_dlyM10 %*% post_intPCTT_gam.cov %*% t(lpmat_dlyM10)))
# sqrt(diag(lpmat_dly %*% post_gam.cov %*% t(lpmat_dly)))
),
stringsAsFactors = F)
post_userProfile <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
labs(y = "Percent Change")+
theme_bw()
legend_userProfile <- g_legend(post_userProfile)
post_userProfile.se <- ggplot(data = pred_df, aes(x = seconds, y = mean, group = user, col = user))+
geom_line()+
geom_line(aes(x=seconds, y = mean + 2*se, group = user, col = user),
linetype = "longdash")+
geom_line(aes(x=seconds, y = mean - 2*se, group = user, col = user),
linetype = "longdash")+
labs(y = "")+
theme_bw()
post_userTraj <- grid.arrange(arrangeGrob(post_userProfile+theme(legend.position = "none"),
post_userProfile.se+theme(legend.position = "none"), nrow = 1),
legend_userProfile, nrow = 1,
widths = c(30, 6))
pred_f1 <- predict(post_gam.fri, newdata=df_pred_occ, se.fit=TRUE, type = "terms")
pred_f2 <- predict(post_gam.fri, newdata=df_pred_dly, se.fit=TRUE, type = "terms")
pred_coef_df <- data.frame(seconds = seq(0, 400)/30,
f0_hat = lpmat_non %*% post_gam.coef,
f0_se = sqrt(diag(lpmat_non %*% post_gam.cov %*% t(lpmat_non))),
f1_hat = pred_f1$fit[, 2],
f1_se = pred_f1$se.fit[, 2],
f2_hat = pred_f2$fit[, 3],
f2_se = pred_f2$se.fit[, 3])
post_non_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f0_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f0_hat + 2*f0_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f0_hat - 2*f0_se), linetype = "longdash")+
labs(title = "Average Percent Change of a Non-user (Post)", ylab = "Percent Change")+
theme_bw()
post_occ_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f1_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f1_hat + 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f1_hat - 2*f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Percent Change: Occasional and Non-user (Post)",
y= "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
post_dly_plt <- ggplot(data = pred_coef_df, aes(x=seconds, y = f2_hat))+
geom_line()+
geom_line(aes(x = seconds, y = f2_hat + 2*f2_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f2_hat - 2*f2_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Percent Change: Daily and Non-user (Post)",
y = "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
ylab <- textGrob(label = "Difference in Percent Change", rot = 90,
gp = gpar(fontsize = 12))
posval <- textGrob(label = sapply(strwrap("Less Constriction in User", width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
negval <- textGrob(label = sapply(strwrap("More Constriction in User", width = 15, simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
post_occ_coef <- grid.arrange(ylab, posval, negval, post_occ_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))
post_dly_coef <- grid.arrange(ylab, posval, negval, post_dly_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)), c(1, 3, rep(4, 15))))Plot difference between daily and occasional user
f2_f1 <- (lpmat_dly - lpmat_occ) %*% post_gam.coef
f2_f1.se <- sqrt(diag((lpmat_dly - lpmat_occ) %*% post_gam.cov %*% t((lpmat_dly - lpmat_occ))))
f2_f1_diff_df <- data.frame(seconds = seq(0, 400)/30,
f2f1_diff = f2_f1,
f2f1_se = f2_f1.se)
ggplot(data = f2_f1_diff_df, aes(x=seconds, y = f2f1_diff))+
geom_line()+
geom_line(aes(x = seconds, y = f2f1_diff + 2*f2f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f2f1_diff - 2*f2f1_se), linetype = "longdash")+
labs(title = "Difference between Average Daily vs Occasional User", ylab = "f2_hat - f1_hat")+
theme_bw()
post_dlyocc_plt <- ggplot(data = f2_f1_diff_df, aes(x=seconds, y = f2f1_diff))+
geom_line()+
geom_line(aes(x = seconds, y = f2f1_diff + 2*f2f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = f2f1_diff - 2*f2f1_se), linetype = "longdash")+
geom_line(aes(x = seconds, y = 0), col = "darkred")+
labs(title = "Difference in Percent Change: Daily vs Occasional User",
y = "")+
theme_bw()+
scale_x_continuous(expand = c(0, 0))+
theme(rect = element_rect(fill = "transparent"))
ylab <- textGrob(label = "Difference in Percent Change", rot = 90,
gp = gpar(fontsize = 12))
posval <- textGrob(label = sapply(strwrap("Less Constriction in Daily User", width = 15,
simplify = F),
paste, collapse = "\n"), rot = 0, just = "bottom",
gp = gpar(fontsize = 8))
negval <- textGrob(label = sapply(strwrap("More Constriction in Daily User", width = 15,
simplify = F),
paste, collapse = "\n"), rot = 0,
gp = gpar(fontsize = 8))
post_dlyocc_coef <- grid.arrange(ylab, posval, negval, post_dlyocc_plt,
ncol = 2, nrow = 2,
layout_matrix = rbind(c(1, 2, rep(4, 15)),
c(1, 3, rep(4, 15))))
post_dlyocc_coef
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